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# Note: The first part of this file can be modified in place, but the latter
# part is autogenerated by the boilerplate.py script.
"""
`matplotlib.pyplot` is a state-based interface to matplotlib. It provides
a MATLAB-like way of plotting.
pyplot is mainly intended for interactive plots and simple cases of
programmatic plot generation::
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 5, 0.1)
y = np.sin(x)
plt.plot(x, y)
The object-oriented API is recommended for more complex plots.
"""
import functools
import importlib
import inspect
import logging
from numbers import Number
import re
import sys
import time
try:
import threading
except ImportError:
import dummy_threading as threading
from cycler import cycler
import matplotlib
import matplotlib.colorbar
import matplotlib.image
from matplotlib import _api
from matplotlib import rcsetup, style
from matplotlib import _pylab_helpers, interactive
from matplotlib import cbook
from matplotlib import docstring
from matplotlib.backend_bases import FigureCanvasBase, MouseButton
from matplotlib.figure import Figure, figaspect
from matplotlib.gridspec import GridSpec, SubplotSpec
from matplotlib import rcParams, rcParamsDefault, get_backend, rcParamsOrig
from matplotlib.rcsetup import interactive_bk as _interactive_bk
from matplotlib.artist import Artist
from matplotlib.axes import Axes, Subplot
from matplotlib.projections import PolarAxes
from matplotlib import mlab # for detrend_none, window_hanning
from matplotlib.scale import get_scale_names
from matplotlib import cm
from matplotlib.cm import get_cmap, register_cmap
import numpy as np
# We may not need the following imports here:
from matplotlib.colors import Normalize
from matplotlib.lines import Line2D
from matplotlib.text import Text, Annotation
from matplotlib.patches import Polygon, Rectangle, Circle, Arrow
from matplotlib.widgets import SubplotTool, Button, Slider, Widget
from .ticker import (
TickHelper, Formatter, FixedFormatter, NullFormatter, FuncFormatter,
FormatStrFormatter, ScalarFormatter, LogFormatter, LogFormatterExponent,
LogFormatterMathtext, Locator, IndexLocator, FixedLocator, NullLocator,
LinearLocator, LogLocator, AutoLocator, MultipleLocator, MaxNLocator)
_log = logging.getLogger(__name__)
_code_objs = {
_api.rename_parameter:
_api.rename_parameter("", "old", "new", lambda new: None).__code__,
_api.make_keyword_only:
_api.make_keyword_only("", "p", lambda p: None).__code__,
}
def _copy_docstring_and_deprecators(method, func=None):
if func is None:
return functools.partial(_copy_docstring_and_deprecators, method)
decorators = [docstring.copy(method)]
# Check whether the definition of *method* includes @_api.rename_parameter
# or @_api.make_keyword_only decorators; if so, propagate them to the
# pyplot wrapper as well.
while getattr(method, "__wrapped__", None) is not None:
for decorator_maker, code in _code_objs.items():
if method.__code__ is code:
kwargs = {
k: v.cell_contents
for k, v in zip(code.co_freevars, method.__closure__)}
assert kwargs["func"] is method.__wrapped__
kwargs.pop("func")
decorators.append(decorator_maker(**kwargs))
method = method.__wrapped__
for decorator in decorators[::-1]:
func = decorator(func)
return func
## Global ##
_IP_REGISTERED = None
_INSTALL_FIG_OBSERVER = False
def install_repl_displayhook():
"""
Install a repl display hook so that any stale figure are automatically
redrawn when control is returned to the repl.
This works both with IPython and with vanilla python shells.
"""
global _IP_REGISTERED
global _INSTALL_FIG_OBSERVER
if _IP_REGISTERED:
return
# See if we have IPython hooks around, if so use them.
# Use ``sys.modules.get(name)`` rather than ``name in sys.modules`` as
# entries can also have been explicitly set to None.
mod_ipython = sys.modules.get("IPython")
if not mod_ipython:
_INSTALL_FIG_OBSERVER = True
return
ip = mod_ipython.get_ipython()
if not ip:
_INSTALL_FIG_OBSERVER = True
return
def post_execute():
if matplotlib.is_interactive():
draw_all()
try: # IPython >= 2
ip.events.register("post_execute", post_execute)
except AttributeError: # IPython 1.x
ip.register_post_execute(post_execute)
_IP_REGISTERED = post_execute
_INSTALL_FIG_OBSERVER = False
from IPython.core.pylabtools import backend2gui
# trigger IPython's eventloop integration, if available
ipython_gui_name = backend2gui.get(get_backend())
if ipython_gui_name:
ip.enable_gui(ipython_gui_name)
def uninstall_repl_displayhook():
"""
Uninstall the Matplotlib display hook.
.. warning::
Need IPython >= 2 for this to work. For IPython < 2 will raise a
``NotImplementedError``
.. warning::
If you are using vanilla python and have installed another
display hook, this will reset ``sys.displayhook`` to what ever
function was there when Matplotlib installed its displayhook,
possibly discarding your changes.
"""
global _IP_REGISTERED
global _INSTALL_FIG_OBSERVER
if _IP_REGISTERED:
from IPython import get_ipython
ip = get_ipython()
try:
ip.events.unregister('post_execute', _IP_REGISTERED)
except AttributeError as err:
raise NotImplementedError("Can not unregister events "
"in IPython < 2.0") from err
_IP_REGISTERED = None
if _INSTALL_FIG_OBSERVER:
_INSTALL_FIG_OBSERVER = False
draw_all = _pylab_helpers.Gcf.draw_all
@functools.wraps(matplotlib.set_loglevel)
def set_loglevel(*args, **kwargs): # Ensure this appears in the pyplot docs.
return matplotlib.set_loglevel(*args, **kwargs)
@_copy_docstring_and_deprecators(Artist.findobj)
def findobj(o=None, match=None, include_self=True):
if o is None:
o = gcf()
return o.findobj(match, include_self=include_self)
def _get_required_interactive_framework(backend_mod):
return getattr(
backend_mod.FigureCanvas, "required_interactive_framework", None)
def switch_backend(newbackend):
"""
Close all open figures and set the Matplotlib backend.
The argument is case-insensitive. Switching to an interactive backend is
possible only if no event loop for another interactive backend has started.
Switching to and from non-interactive backends is always possible.
Parameters
----------
newbackend : str
The name of the backend to use.
"""
global _backend_mod
# make sure the init is pulled up so we can assign to it later
import matplotlib.backends
close("all")
if newbackend is rcsetup._auto_backend_sentinel:
current_framework = cbook._get_running_interactive_framework()
mapping = {'qt5': 'qt5agg',
'qt4': 'qt4agg',
'gtk3': 'gtk3agg',
'wx': 'wxagg',
'tk': 'tkagg',
'macosx': 'macosx',
'headless': 'agg'}
best_guess = mapping.get(current_framework, None)
if best_guess is not None:
candidates = [best_guess]
else:
candidates = []
candidates += ["macosx", "qt5agg", "gtk3agg", "tkagg", "wxagg"]
# Don't try to fallback on the cairo-based backends as they each have
# an additional dependency (pycairo) over the agg-based backend, and
# are of worse quality.
for candidate in candidates:
try:
switch_backend(candidate)
except ImportError:
continue
else:
rcParamsOrig['backend'] = candidate
return
else:
# Switching to Agg should always succeed; if it doesn't, let the
# exception propagate out.
switch_backend("agg")
rcParamsOrig["backend"] = "agg"
return
# Backends are implemented as modules, but "inherit" default method
# implementations from backend_bases._Backend. This is achieved by
# creating a "class" that inherits from backend_bases._Backend and whose
# body is filled with the module's globals.
backend_name = cbook._backend_module_name(newbackend)
class backend_mod(matplotlib.backend_bases._Backend):
locals().update(vars(importlib.import_module(backend_name)))
required_framework = _get_required_interactive_framework(backend_mod)
if required_framework is not None:
current_framework = cbook._get_running_interactive_framework()
if (current_framework and required_framework
and current_framework != required_framework):
raise ImportError(
"Cannot load backend {!r} which requires the {!r} interactive "
"framework, as {!r} is currently running".format(
newbackend, required_framework, current_framework))
_log.debug("Loaded backend %s version %s.",
newbackend, backend_mod.backend_version)
rcParams['backend'] = rcParamsDefault['backend'] = newbackend
_backend_mod = backend_mod
for func_name in ["new_figure_manager", "draw_if_interactive", "show"]:
globals()[func_name].__signature__ = inspect.signature(
getattr(backend_mod, func_name))
# Need to keep a global reference to the backend for compatibility reasons.
# See https://github.com/matplotlib/matplotlib/issues/6092
matplotlib.backends.backend = newbackend
def _warn_if_gui_out_of_main_thread():
if (_get_required_interactive_framework(_backend_mod)
and threading.current_thread() is not threading.main_thread()):
_api.warn_external(
"Starting a Matplotlib GUI outside of the main thread will likely "
"fail.")
# This function's signature is rewritten upon backend-load by switch_backend.
def new_figure_manager(*args, **kwargs):
"""Create a new figure manager instance."""
_warn_if_gui_out_of_main_thread()
return _backend_mod.new_figure_manager(*args, **kwargs)
# This function's signature is rewritten upon backend-load by switch_backend.
def draw_if_interactive(*args, **kwargs):
"""
Redraw the current figure if in interactive mode.
.. warning::
End users will typically not have to call this function because the
the interactive mode takes care of this.
"""
return _backend_mod.draw_if_interactive(*args, **kwargs)
# This function's signature is rewritten upon backend-load by switch_backend.
def show(*args, **kwargs):
"""
Display all open figures.
Parameters
----------
block : bool, optional
Whether to wait for all figures to be closed before returning.
If `True` block and run the GUI main loop until all figure windows
are closed.
If `False` ensure that all figure windows are displayed and return
immediately. In this case, you are responsible for ensuring
that the event loop is running to have responsive figures.
Defaults to True in non-interactive mode and to False in interactive
mode (see `.pyplot.isinteractive`).
See Also
--------
ion : Enable interactive mode, which shows / updates the figure after
every plotting command, so that calling ``show()`` is not necessary.
ioff : Disable interactive mode.
savefig : Save the figure to an image file instead of showing it on screen.
Notes
-----
**Saving figures to file and showing a window at the same time**
If you want an image file as well as a user interface window, use
`.pyplot.savefig` before `.pyplot.show`. At the end of (a blocking)
``show()`` the figure is closed and thus unregistered from pyplot. Calling
`.pyplot.savefig` afterwards would save a new and thus empty figure. This
limitation of command order does not apply if the show is non-blocking or
if you keep a reference to the figure and use `.Figure.savefig`.
**Auto-show in jupyter notebooks**
The jupyter backends (activated via ``%matplotlib inline``,
``%matplotlib notebook``, or ``%matplotlib widget``), call ``show()`` at
the end of every cell by default. Thus, you usually don't have to call it
explicitly there.
"""
_warn_if_gui_out_of_main_thread()
return _backend_mod.show(*args, **kwargs)
def isinteractive():
"""
Return whether plots are updated after every plotting command.
The interactive mode is mainly useful if you build plots from the command
line and want to see the effect of each command while you are building the
figure.
In interactive mode:
- newly created figures will be shown immediately;
- figures will automatically redraw on change;
- `.pyplot.show` will not block by default.
In non-interactive mode:
- newly created figures and changes to figures will not be reflected until
explicitly asked to be;
- `.pyplot.show` will block by default.
See Also
--------
ion : Enable interactive mode.
ioff : Disable interactive mode.
show : Show all figures (and maybe block).
pause : Show all figures, and block for a time.
"""
return matplotlib.is_interactive()
class _IoffContext:
"""
Context manager for `.ioff`.
The state is changed in ``__init__()`` instead of ``__enter__()``. The
latter is a no-op. This allows using `.ioff` both as a function and
as a context.
"""
def __init__(self):
self.wasinteractive = isinteractive()
matplotlib.interactive(False)
uninstall_repl_displayhook()
def __enter__(self):
pass
def __exit__(self, exc_type, exc_value, traceback):
if self.wasinteractive:
matplotlib.interactive(True)
install_repl_displayhook()
else:
matplotlib.interactive(False)
uninstall_repl_displayhook()
class _IonContext:
"""
Context manager for `.ion`.
The state is changed in ``__init__()`` instead of ``__enter__()``. The
latter is a no-op. This allows using `.ion` both as a function and
as a context.
"""
def __init__(self):
self.wasinteractive = isinteractive()
matplotlib.interactive(True)
install_repl_displayhook()
def __enter__(self):
pass
def __exit__(self, exc_type, exc_value, traceback):
if not self.wasinteractive:
matplotlib.interactive(False)
uninstall_repl_displayhook()
else:
matplotlib.interactive(True)
install_repl_displayhook()
def ioff():
"""
Disable interactive mode.
See `.pyplot.isinteractive` for more details.
See Also
--------
ion : Enable interactive mode.
isinteractive : Whether interactive mode is enabled.
show : Show all figures (and maybe block).
pause : Show all figures, and block for a time.
Notes
-----
For a temporary change, this can be used as a context manager::
# if interactive mode is on
# then figures will be shown on creation
plt.ion()
# This figure will be shown immediately
fig = plt.figure()
with plt.ioff():
# interactive mode will be off
# figures will not automatically be shown
fig2 = plt.figure()
# ...
To enable usage as a context manager, this function returns an
``_IoffContext`` object. The return value is not intended to be stored
or accessed by the user.
"""
return _IoffContext()
def ion():
"""
Enable interactive mode.
See `.pyplot.isinteractive` for more details.
See Also
--------
ioff : Disable interactive mode.
isinteractive : Whether interactive mode is enabled.
show : Show all figures (and maybe block).
pause : Show all figures, and block for a time.
Notes
-----
For a temporary change, this can be used as a context manager::
# if interactive mode is off
# then figures will not be shown on creation
plt.ioff()
# This figure will not be shown immediately
fig = plt.figure()
with plt.ion():
# interactive mode will be on
# figures will automatically be shown
fig2 = plt.figure()
# ...
To enable usage as a context manager, this function returns an
``_IonContext`` object. The return value is not intended to be stored
or accessed by the user.
"""
return _IonContext()
def pause(interval):
"""
Run the GUI event loop for *interval* seconds.
If there is an active figure, it will be updated and displayed before the
pause, and the GUI event loop (if any) will run during the pause.
This can be used for crude animation. For more complex animation use
:mod:`matplotlib.animation`.
If there is no active figure, sleep for *interval* seconds instead.
See Also
--------
matplotlib.animation : Proper animations
show : Show all figures and optional block until all figures are closed.
"""
manager = _pylab_helpers.Gcf.get_active()
if manager is not None:
canvas = manager.canvas
if canvas.figure.stale:
canvas.draw_idle()
show(block=False)
canvas.start_event_loop(interval)
else:
time.sleep(interval)
@_copy_docstring_and_deprecators(matplotlib.rc)
def rc(group, **kwargs):
matplotlib.rc(group, **kwargs)
@_copy_docstring_and_deprecators(matplotlib.rc_context)
def rc_context(rc=None, fname=None):
return matplotlib.rc_context(rc, fname)
@_copy_docstring_and_deprecators(matplotlib.rcdefaults)
def rcdefaults():
matplotlib.rcdefaults()
if matplotlib.is_interactive():
draw_all()
# getp/get/setp are explicitly reexported so that they show up in pyplot docs.
@_copy_docstring_and_deprecators(matplotlib.artist.getp)
def getp(obj, *args, **kwargs):
return matplotlib.artist.getp(obj, *args, **kwargs)
@_copy_docstring_and_deprecators(matplotlib.artist.get)
def get(obj, *args, **kwargs):
return matplotlib.artist.get(obj, *args, **kwargs)
@_copy_docstring_and_deprecators(matplotlib.artist.setp)
def setp(obj, *args, **kwargs):
return matplotlib.artist.setp(obj, *args, **kwargs)
def xkcd(scale=1, length=100, randomness=2):
"""
Turn on `xkcd <https://xkcd.com/>`_ sketch-style drawing mode. This will
only have effect on things drawn after this function is called.
For best results, the "Humor Sans" font should be installed: it is
not included with Matplotlib.
Parameters
----------
scale : float, optional
The amplitude of the wiggle perpendicular to the source line.
length : float, optional
The length of the wiggle along the line.
randomness : float, optional
The scale factor by which the length is shrunken or expanded.
Notes
-----
This function works by a number of rcParams, so it will probably
override others you have set before.
If you want the effects of this function to be temporary, it can
be used as a context manager, for example::
with plt.xkcd():
# This figure will be in XKCD-style
fig1 = plt.figure()
# ...
# This figure will be in regular style
fig2 = plt.figure()
"""
return _xkcd(scale, length, randomness)
class _xkcd:
# This cannot be implemented in terms of rc_context() because this needs to
# work as a non-contextmanager too.
def __init__(self, scale, length, randomness):
self._orig = rcParams.copy()
if rcParams['text.usetex']:
raise RuntimeError(
"xkcd mode is not compatible with text.usetex = True")
from matplotlib import patheffects
rcParams.update({
'font.family': ['xkcd', 'xkcd Script', 'Humor Sans', 'Comic Neue',
'Comic Sans MS'],
'font.size': 14.0,
'path.sketch': (scale, length, randomness),
'path.effects': [
patheffects.withStroke(linewidth=4, foreground="w")],
'axes.linewidth': 1.5,
'lines.linewidth': 2.0,
'figure.facecolor': 'white',
'grid.linewidth': 0.0,
'axes.grid': False,
'axes.unicode_minus': False,
'axes.edgecolor': 'black',
'xtick.major.size': 8,
'xtick.major.width': 3,
'ytick.major.size': 8,
'ytick.major.width': 3,
})
def __enter__(self):
return self
def __exit__(self, *args):
dict.update(rcParams, self._orig)
## Figures ##
def figure(num=None, # autoincrement if None, else integer from 1-N
figsize=None, # defaults to rc figure.figsize
dpi=None, # defaults to rc figure.dpi
facecolor=None, # defaults to rc figure.facecolor
edgecolor=None, # defaults to rc figure.edgecolor
frameon=True,
FigureClass=Figure,
clear=False,
**kwargs
):
"""
Create a new figure, or activate an existing figure.
Parameters
----------
num : int or str or `.Figure`, optional
A unique identifier for the figure.
If a figure with that identifier already exists, this figure is made
active and returned. An integer refers to the ``Figure.number``
attribute, a string refers to the figure label.
If there is no figure with the identifier or *num* is not given, a new
figure is created, made active and returned. If *num* is an int, it
will be used for the ``Figure.number`` attribute, otherwise, an
auto-generated integer value is used (starting at 1 and incremented
for each new figure). If *num* is a string, the figure label and the
window title is set to this value.
figsize : (float, float), default: :rc:`figure.figsize`
Width, height in inches.
dpi : float, default: :rc:`figure.dpi`
The resolution of the figure in dots-per-inch.
facecolor : color, default: :rc:`figure.facecolor`
The background color.
edgecolor : color, default: :rc:`figure.edgecolor`
The border color.
frameon : bool, default: True
If False, suppress drawing the figure frame.
FigureClass : subclass of `~matplotlib.figure.Figure`
Optionally use a custom `.Figure` instance.
clear : bool, default: False
If True and the figure already exists, then it is cleared.
tight_layout : bool or dict, default: :rc:`figure.autolayout`
If ``False`` use *subplotpars*. If ``True`` adjust subplot
parameters using `.tight_layout` with default padding.
When providing a dict containing the keys ``pad``, ``w_pad``,
``h_pad``, and ``rect``, the default `.tight_layout` paddings
will be overridden.
constrained_layout : bool, default: :rc:`figure.constrained_layout.use`
If ``True`` use constrained layout to adjust positioning of plot
elements. Like ``tight_layout``, but designed to be more
flexible. See
:doc:`/tutorials/intermediate/constrainedlayout_guide`
for examples. (Note: does not work with `add_subplot` or
`~.pyplot.subplot2grid`.)
**kwargs : optional
See `~.matplotlib.figure.Figure` for other possible arguments.
Returns
-------
`~matplotlib.figure.Figure`
The `.Figure` instance returned will also be passed to
new_figure_manager in the backends, which allows to hook custom
`.Figure` classes into the pyplot interface. Additional kwargs will be
passed to the `.Figure` init function.
Notes
-----
If you are creating many figures, make sure you explicitly call
`.pyplot.close` on the figures you are not using, because this will
enable pyplot to properly clean up the memory.
`~matplotlib.rcParams` defines the default values, which can be modified
in the matplotlibrc file.
"""
if isinstance(num, Figure):
if num.canvas.manager is None:
raise ValueError("The passed figure is not managed by pyplot")
_pylab_helpers.Gcf.set_active(num.canvas.manager)
return num
allnums = get_fignums()
next_num = max(allnums) + 1 if allnums else 1
fig_label = ''
if num is None:
num = next_num
elif isinstance(num, str):
fig_label = num
all_labels = get_figlabels()
if fig_label not in all_labels:
if fig_label == 'all':
_api.warn_external("close('all') closes all existing figures.")
num = next_num
else:
inum = all_labels.index(fig_label)
num = allnums[inum]
else:
num = int(num) # crude validation of num argument
manager = _pylab_helpers.Gcf.get_fig_manager(num)
if manager is None:
max_open_warning = rcParams['figure.max_open_warning']
if len(allnums) == max_open_warning >= 1:
_api.warn_external(
f"More than {max_open_warning} figures have been opened. "
f"Figures created through the pyplot interface "
f"(`matplotlib.pyplot.figure`) are retained until explicitly "
f"closed and may consume too much memory. (To control this "
f"warning, see the rcParam `figure.max_open_warning`).",
RuntimeWarning)
manager = new_figure_manager(
num, figsize=figsize, dpi=dpi,
facecolor=facecolor, edgecolor=edgecolor, frameon=frameon,
FigureClass=FigureClass, **kwargs)
fig = manager.canvas.figure
if fig_label:
fig.set_label(fig_label)
_pylab_helpers.Gcf._set_new_active_manager(manager)
# make sure backends (inline) that we don't ship that expect this
# to be called in plotting commands to make the figure call show
# still work. There is probably a better way to do this in the
# FigureManager base class.
draw_if_interactive()
if _INSTALL_FIG_OBSERVER:
fig.stale_callback = _auto_draw_if_interactive
if clear:
manager.canvas.figure.clear()
return manager.canvas.figure
def _auto_draw_if_interactive(fig, val):
"""
An internal helper function for making sure that auto-redrawing
works as intended in the plain python repl.
Parameters
----------
fig : Figure
A figure object which is assumed to be associated with a canvas
"""
if (val and matplotlib.is_interactive()
and not fig.canvas.is_saving()
and not fig.canvas._is_idle_drawing):
# Some artists can mark themselves as stale in the middle of drawing
# (e.g. axes position & tick labels being computed at draw time), but
# this shouldn't trigger a redraw because the current redraw will
# already take them into account.
with fig.canvas._idle_draw_cntx():
fig.canvas.draw_idle()
def gcf():
"""
Get the current figure.
If no current figure exists, a new one is created using
`~.pyplot.figure()`.
"""
manager = _pylab_helpers.Gcf.get_active()
if manager is not None:
return manager.canvas.figure
else:
return figure()
def fignum_exists(num):
"""Return whether the figure with the given id exists."""
return _pylab_helpers.Gcf.has_fignum(num) or num in get_figlabels()
def get_fignums():
"""Return a list of existing figure numbers."""
return sorted(_pylab_helpers.Gcf.figs)
def get_figlabels():
"""Return a list of existing figure labels."""
managers = _pylab_helpers.Gcf.get_all_fig_managers()
managers.sort(key=lambda m: m.num)
return [m.canvas.figure.get_label() for m in managers]
def get_current_fig_manager():
"""
Return the figure manager of the current figure.
The figure manager is a container for the actual backend-depended window
that displays the figure on screen.
If no current figure exists, a new one is created, and its figure
manager is returned.
Returns
-------
`.FigureManagerBase` or backend-dependent subclass thereof
"""
return gcf().canvas.manager
@_copy_docstring_and_deprecators(FigureCanvasBase.mpl_connect)
def connect(s, func):
return gcf().canvas.mpl_connect(s, func)
@_copy_docstring_and_deprecators(FigureCanvasBase.mpl_disconnect)
def disconnect(cid):
return gcf().canvas.mpl_disconnect(cid)
def close(fig=None):
"""
Close a figure window.
Parameters
----------
fig : None or int or str or `.Figure`
The figure to close. There are a number of ways to specify this:
- *None*: the current figure
- `.Figure`: the given `.Figure` instance
- ``int``: a figure number
- ``str``: a figure name
- 'all': all figures
"""
if fig is None:
manager = _pylab_helpers.Gcf.get_active()
if manager is None:
return
else:
_pylab_helpers.Gcf.destroy(manager)
elif fig == 'all':
_pylab_helpers.Gcf.destroy_all()
elif isinstance(fig, int):
_pylab_helpers.Gcf.destroy(fig)
elif hasattr(fig, 'int'):
# if we are dealing with a type UUID, we
# can use its integer representation
_pylab_helpers.Gcf.destroy(fig.int)
elif isinstance(fig, str):
all_labels = get_figlabels()
if fig in all_labels:
num = get_fignums()[all_labels.index(fig)]
_pylab_helpers.Gcf.destroy(num)
elif isinstance(fig, Figure):
_pylab_helpers.Gcf.destroy_fig(fig)
else:
raise TypeError("close() argument must be a Figure, an int, a string, "
"or None, not %s" % type(fig))
def clf():
"""Clear the current figure."""
gcf().clf()
def draw():
"""
Redraw the current figure.
This is used to update a figure that has been altered, but not
automatically re-drawn. If interactive mode is on (via `.ion()`), this
should be only rarely needed, but there may be ways to modify the state of
a figure without marking it as "stale". Please report these cases as bugs.
This is equivalent to calling ``fig.canvas.draw_idle()``, where ``fig`` is
the current figure.
"""
gcf().canvas.draw_idle()
@_copy_docstring_and_deprecators(Figure.savefig)
def savefig(*args, **kwargs):
fig = gcf()
res = fig.savefig(*args, **kwargs)
fig.canvas.draw_idle() # need this if 'transparent=True' to reset colors
return res
## Putting things in figures ##
def figlegend(*args, **kwargs):
return gcf().legend(*args, **kwargs)
if Figure.legend.__doc__:
figlegend.__doc__ = Figure.legend.__doc__.replace("legend(", "figlegend(")
## Axes ##
@docstring.dedent_interpd
def axes(arg=None, **kwargs):
"""
Add an axes to the current figure and make it the current axes.
Call signatures::
plt.axes()
plt.axes(rect, projection=None, polar=False, **kwargs)
plt.axes(ax)
Parameters
----------
arg : None or 4-tuple
The exact behavior of this function depends on the type:
- *None*: A new full window axes is added using
``subplot(**kwargs)``.
- 4-tuple of floats *rect* = ``[left, bottom, width, height]``.
A new axes is added with dimensions *rect* in normalized
(0, 1) units using `~.Figure.add_axes` on the current figure.
projection : {None, 'aitoff', 'hammer', 'lambert', 'mollweide', \
'polar', 'rectilinear', str}, optional
The projection type of the `~.axes.Axes`. *str* is the name of
a custom projection, see `~matplotlib.projections`. The default
None results in a 'rectilinear' projection.
polar : bool, default: False
If True, equivalent to projection='polar'.
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