matplotlib/lib/matplotlib/pyplot.py at v3.11.2 · matplotlib/matplotlib · GitHub

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# Note: The first part of this file is hand-written and must be edited

# in-place. The second part, starting with

# ### REMAINING CONTENT GENERATED BY boilerplate.py ###

# is generated by the script boilerplate.py. It must not be edited here

# because all changes will be overwritten by the next run of the script.

# For more information see the description in boilerplate.py.

"""

`matplotlib.pyplot` is a state-based interface to matplotlib. It provides

an implicit, MATLAB-like, way of plotting. It also opens figures on your

screen, and acts as the figure GUI manager.

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)

plt.show()

The explicit object-oriented API is recommended for complex plots, though

pyplot is still usually used to create the figure and often the Axes in the

figure. See `.pyplot.figure`, `.pyplot.subplots`, and

`.pyplot.subplot_mosaic` to create figures, and

:doc:`Axes API </api/axes_api>` for the plotting methods on an Axes::

import numpy as np

import matplotlib.pyplot as plt

x = np.arange(0, 5, 0.1)

y = np.sin(x)

fig, ax = plt.subplots()

ax.plot(x, y)

plt.show()

See :ref:`api_interfaces` for an explanation of the tradeoffs between the

implicit and explicit interfaces.

"""

# fmt: off

from __future__ import annotations

from contextlib import AbstractContextManager, ExitStack

from enum import Enum

import functools

import importlib

import inspect

import logging

import sys

import threading

import time

from typing import IO, TYPE_CHECKING, cast, overload

from cycler import cycler # noqa: F401

import matplotlib

import matplotlib.image

from matplotlib import _api

# Re-exported (import x as x) for typing.

from matplotlib import get_backend as get_backend, rcParams as rcParams

from matplotlib import cm as cm # noqa: F401

from matplotlib import style as style # noqa: F401

from matplotlib import _pylab_helpers

from matplotlib import interactive # noqa: F401

from matplotlib import cbook

from matplotlib import _docstring

from matplotlib.backend_bases import (

FigureCanvasBase, FigureManagerBase, MouseButton)

from matplotlib.figure import Figure, FigureBase, figaspect

from matplotlib.gridspec import GridSpec, SubplotSpec

from matplotlib import rcsetup, rcParamsDefault, rcParamsOrig

from matplotlib.artist import Artist

from matplotlib.axes import Axes

from matplotlib.axes import Subplot # noqa: F401

from matplotlib.backends import BackendFilter, backend_registry

from matplotlib.projections import PolarAxes

from matplotlib.colorizer import _ColorizerInterface, ColorizingArtist, Colorizer

from matplotlib import mlab # for detrend_none, window_hanning

from matplotlib.scale import get_scale_names # noqa: F401

from matplotlib.cm import _colormaps

from matplotlib.colors import _color_sequences, Colormap

import numpy as np

if TYPE_CHECKING:

from collections.abc import Callable, Hashable, Iterable, Sequence

import pathlib

import os

from typing import Any, BinaryIO, Literal, TypeVar

from typing_extensions import ParamSpec

import PIL.Image

from numpy.typing import ArrayLike

import pandas as pd

import matplotlib.axes

import matplotlib.artist

import matplotlib.backend_bases

from matplotlib.axis import Tick

from matplotlib.axes._base import _AxesBase

from matplotlib.backend_bases import (

CloseEvent,

DrawEvent,

KeyEvent,

MouseEvent,

PickEvent,

ResizeEvent,

)

from matplotlib.cm import ScalarMappable

from matplotlib.contour import ContourSet, QuadContourSet

from matplotlib.collections import (

Collection,

FillBetweenPolyCollection,

LineCollection,

PolyCollection,

PathCollection,

EventCollection,

QuadMesh,

)

from matplotlib.colorbar import Colorbar

from matplotlib.container import (

BarContainer,

ErrorbarContainer,

PieContainer,

StemContainer,

)

from matplotlib.figure import SubFigure

from matplotlib.legend import Legend

from matplotlib.mlab import GaussianKDE

from matplotlib.image import AxesImage, FigureImage

from matplotlib.patches import FancyArrow, StepPatch

from matplotlib.quiver import Barbs, Quiver, QuiverKey

from matplotlib.scale import ScaleBase

from matplotlib.typing import (

CloseEventType,

ColorType,

CoordsType,

DataParamType,

DrawEventType,

HashableList,

KeyEventType,

LineStyleType,

MarkerType,

MouseEventType,

PickEventType,

RcGroupKeyType,

RcKeyType,

ResizeEventType,

LogLevel

)

from matplotlib.widgets import SubplotTool

_P = ParamSpec('_P')

_R = TypeVar('_R')

_T = TypeVar('_T')

# We may not need the following imports here:

from matplotlib.colors import Normalize

from matplotlib.lines import Line2D, AxLine

from matplotlib.text import Text, Annotation

from matplotlib.patches import Arrow, Circle, Rectangle # noqa: F401

from matplotlib.patches import Polygon

from matplotlib.widgets import Button, Slider, Widget # noqa: F401

from .ticker import ( # noqa: F401

TickHelper, Formatter, FixedFormatter, NullFormatter, FuncFormatter,

FormatStrFormatter, ScalarFormatter, LogFormatter, LogFormatterExponent,

LogFormatterMathtext, Locator, IndexLocator, FixedLocator, NullLocator,

LinearLocator, LogLocator, AutoLocator, MultipleLocator, MaxNLocator)

_log = logging.getLogger(__name__)

# Explicit rename instead of import-as for typing's sake.

colormaps = _colormaps

color_sequences = _color_sequences

@overload

def _copy_docstring_and_deprecators(

method: Any,

func: Literal[None] = None

) -> Callable[[Callable[_P, _R]], Callable[_P, _R]]: ...

@overload

def _copy_docstring_and_deprecators(

method: Any, func: Callable[_P, _R]) -> Callable[_P, _R]: ...

def _copy_docstring_and_deprecators(

method: Any,

func: Callable[_P, _R] | None = None

) -> Callable[[Callable[_P, _R]], Callable[_P, _R]] | Callable[_P, _R]:

if func is None:

return cast('Callable[[Callable[_P, _R]], Callable[_P, _R]]',

functools.partial(_copy_docstring_and_deprecators, method))

decorators: list[Callable[[Callable[_P, _R]], Callable[_P, _R]]] = [

_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 hasattr(method, "__wrapped__"):

potential_decorator = _api.deprecation.DECORATORS.get(method)

if potential_decorator:

decorators.append(potential_decorator)

method = method.__wrapped__

for decorator in decorators[::-1]:

func = decorator(func)

_add_pyplot_note(func, method)

return func

_NO_PYPLOT_NOTE = [

'FigureBase._gci', # wrapped_func is private

'_AxesBase._sci', # wrapped_func is private

'Artist.findobj', # not a standard pyplot wrapper because it does not operate

# on the current Figure / Axes. Explanation of relation would

# be more complex and is not too important.

]

def _add_pyplot_note(func, wrapped_func):

"""

Add a note to the docstring of *func* that it is a pyplot wrapper.

The note is added to the "Notes" section of the docstring. If that does

not exist, a "Notes" section is created. In numpydoc, the "Notes"

section is the third last possible section, only potentially followed by

"References" and "Examples".

"""

if not func.__doc__:

return # nothing to do

qualname = wrapped_func.__qualname__

if qualname in _NO_PYPLOT_NOTE:

return

wrapped_func_is_method = True

if "." not in qualname:

# method qualnames are prefixed by the class and ".", e.g. "Axes.plot"

wrapped_func_is_method = False

link = f"{wrapped_func.__module__}.{qualname}"

elif qualname.startswith("Axes."): # e.g. "Axes.plot"

link = ".axes." + qualname

elif qualname.startswith("_AxesBase."): # e.g. "_AxesBase.set_xlabel"

link = ".axes.Axes" + qualname[9:]

elif qualname.startswith("Figure."): # e.g. "Figure.figimage"

link = "." + qualname

elif qualname.startswith("FigureBase."): # e.g. "FigureBase.gca"

link = ".Figure" + qualname[10:]

elif qualname.startswith("FigureCanvasBase."): # "FigureBaseCanvas.mpl_connect"

link = "." + qualname

else:

raise RuntimeError(f"Wrapped method from unexpected class: {qualname}")

if wrapped_func_is_method:

message = f"This is the :ref:`pyplot wrapper <pyplot_interface>` for `{link}`."

else:

message = f"This is equivalent to `{link}`."

# Find the correct insert position:

# - either we already have a "Notes" section into which we can insert

# - or we create one before the next present section. Note that in numpydoc, the

# "Notes" section is the third last possible section, only potentially followed

# by "References" and "Examples".

# - or we append a new "Notes" section at the end.

doc = inspect.cleandoc(func.__doc__)

if "\nNotes\n-----" in doc:

before, after = doc.split("\nNotes\n-----", 1)

elif (index := doc.find("\nReferences\n----------")) != -1:

before, after = doc[:index], doc[index:]

elif (index := doc.find("\nExamples\n--------")) != -1:

before, after = doc[:index], doc[index:]

else:

# No "Notes", "References", or "Examples" --> append to the end.

before = doc + "\n"

after = ""

func.__doc__ = f"{before}\nNotes\n-----\n\n.. note::\n\n {message}\n{after}"

## Global ##

# The state controlled by {,un}install_repl_displayhook().

_ReplDisplayHook = Enum("_ReplDisplayHook", ["NONE", "PLAIN", "IPYTHON"])

_REPL_DISPLAYHOOK = _ReplDisplayHook.NONE

def _draw_all_if_interactive() -> None:

if matplotlib.is_interactive():

draw_all()

def install_repl_displayhook() -> None:

"""

Connect to the display hook of the current shell.

The display hook gets called when the read-evaluate-print-loop (REPL) of

the shell has finished the execution of a command. We use this callback

to be able to automatically update a figure in interactive mode.

This works both with IPython and with vanilla python shells.

"""

global _REPL_DISPLAYHOOK

if _REPL_DISPLAYHOOK is _ReplDisplayHook.IPYTHON:

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:

_REPL_DISPLAYHOOK = _ReplDisplayHook.PLAIN

return

ip = mod_ipython.get_ipython()

if not ip:

_REPL_DISPLAYHOOK = _ReplDisplayHook.PLAIN

return

ip.events.register("post_execute", _draw_all_if_interactive)

_REPL_DISPLAYHOOK = _ReplDisplayHook.IPYTHON

if mod_ipython.version_info[:2] < (8, 24):

# Use of backend2gui is not needed for IPython >= 8.24 as that functionality

# has been moved to Matplotlib.

# This code can be removed when Python 3.12, the latest version supported by

# IPython < 8.24, reaches end-of-life in late 2028.

from IPython.core.pylabtools import backend2gui

ipython_gui_name = backend2gui.get(get_backend())

else:

_, ipython_gui_name = backend_registry.resolve_backend(get_backend())

# trigger IPython's eventloop integration, if available

if ipython_gui_name:

ip.enable_gui(ipython_gui_name)

def uninstall_repl_displayhook() -> None:

"""Disconnect from the display hook of the current shell."""

global _REPL_DISPLAYHOOK

if _REPL_DISPLAYHOOK is _ReplDisplayHook.IPYTHON:

from IPython import get_ipython

ip = get_ipython()

ip.events.unregister("post_execute", _draw_all_if_interactive)

_REPL_DISPLAYHOOK = _ReplDisplayHook.NONE

draw_all = _pylab_helpers.Gcf.draw_all

# Ensure this appears in the pyplot docs.

@_copy_docstring_and_deprecators(matplotlib.set_loglevel)

def set_loglevel(level: LogLevel) -> None:

return matplotlib.set_loglevel(level)

@_copy_docstring_and_deprecators(Artist.findobj)

def findobj(

o: Artist | None = None,

match: Callable[[Artist], bool] | type[Artist] | None = None,

include_self: bool = True

) -> list[Artist]:

if o is None:

o = gcf()

return o.findobj(match, include_self=include_self)

_backend_mod: type[matplotlib.backend_bases._Backend] | None = None

def _get_backend_mod() -> type[matplotlib.backend_bases._Backend]:

"""

Ensure that a backend is selected and return it.

This is currently private, but may be made public in the future.

"""

if _backend_mod is None:

# Use rcParams._get("backend") to avoid going through the fallback

# logic (which will (re)import pyplot and then call switch_backend if

# we need to resolve the auto sentinel)

switch_backend(rcParams._get("backend"))

return cast(type[matplotlib.backend_bases._Backend], _backend_mod)

def switch_backend(newbackend: str) -> None:

"""

Set the pyplot backend.

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 case-insensitive 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

if newbackend is rcsetup._auto_backend_sentinel:

current_framework = cbook._get_running_interactive_framework()

if (current_framework and

(backend := backend_registry.backend_for_gui_framework(

current_framework))):

candidates = [backend]

else:

candidates = []

candidates += [

"macosx", "qtagg", "gtk4agg", "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:

_log.debug("Skipping backend candidate %r as loading failed.",

candidate, exc_info=True)

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

old_backend = rcParams._get('backend') # get without triggering backend resolution

module = backend_registry.load_backend_module(newbackend)

canvas_class = module.FigureCanvas

required_framework = canvas_class.required_interactive_framework

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))

# Load the new_figure_manager() and show() functions from the backend.

# Classically, backends can directly export these functions. This should

# keep working for backcompat.

new_figure_manager = getattr(module, "new_figure_manager", None)

show = getattr(module, "show", None)

# In that classical approach, 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 globals.

class backend_mod(matplotlib.backend_bases._Backend):

locals().update(vars(module))

# However, the newer approach for defining new_figure_manager and

# show is to derive them from canvas methods. In that case, also

# update backend_mod accordingly; also, per-backend customization of

# draw_if_interactive is disabled.

if new_figure_manager is None:

def new_figure_manager_given_figure(num, figure):

return canvas_class.new_manager(figure, num)

def new_figure_manager(num, *args, FigureClass=Figure, **kwargs):

fig = FigureClass(*args, **kwargs)

return new_figure_manager_given_figure(num, fig)

def draw_if_interactive() -> None:

if matplotlib.is_interactive():

manager = _pylab_helpers.Gcf.get_active()

if manager:

manager.canvas.draw_idle()

backend_mod.new_figure_manager_given_figure = ( # type: ignore[method-assign]

new_figure_manager_given_figure)

backend_mod.new_figure_manager = ( # type: ignore[method-assign]

new_figure_manager)

backend_mod.draw_if_interactive = ( # type: ignore[method-assign]

draw_if_interactive)

# If the manager explicitly overrides pyplot_show, use it even if a global

# show is already present, as the latter may be here for backcompat.

manager_class = getattr(canvas_class, "manager_class", None)

# We can't compare directly manager_class.pyplot_show and FMB.pyplot_show because

# pyplot_show is a classmethod so the above constructs are bound classmethods, and

# thus always different (being bound to different classes). We also have to use

# getattr_static instead of vars as manager_class could have no __dict__.

manager_pyplot_show = inspect.getattr_static(manager_class, "pyplot_show", None)

base_pyplot_show = inspect.getattr_static(FigureManagerBase, "pyplot_show", None)

if (show is None

or (manager_pyplot_show is not None

and manager_pyplot_show != base_pyplot_show)):

if not manager_pyplot_show:

raise ValueError(

f"Backend {newbackend} defines neither FigureCanvas.manager_class nor "

f"a toplevel show function")

_pyplot_show = cast('Any', manager_class).pyplot_show

backend_mod.show = _pyplot_show # type: ignore[method-assign]

_log.debug("Loaded backend %s version %s.",

newbackend, backend_mod.backend_version)

if newbackend in ("ipympl", "widget"):

# ipympl < 0.9.4 expects rcParams["backend"] to be the fully-qualified backend

# name "module://ipympl.backend_nbagg" not short names "ipympl" or "widget".

import importlib.metadata as im

from matplotlib import _parse_to_version_info # type: ignore[attr-defined]

try:

module_version = im.version("ipympl")

if _parse_to_version_info(module_version) < (0, 9, 4):

newbackend = "module://ipympl.backend_nbagg"

except im.PackageNotFoundError:

pass

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 # type: ignore[attr-defined]

# Make sure the repl display hook is installed in case we become interactive.

try:

install_repl_displayhook()

except NotImplementedError as err:

_log.warning("Fallback to a different backend")

raise ImportError from err

def _warn_if_gui_out_of_main_thread() -> None:

warn = False

canvas_class = cast(type[FigureCanvasBase], _get_backend_mod().FigureCanvas)

if canvas_class.required_interactive_framework:

if hasattr(threading, 'get_native_id'):

# This compares native thread ids because even if Python-level

# Thread objects match, the underlying OS thread (which is what

# really matters) may be different on Python implementations with

# green threads.

if threading.get_native_id() != threading.main_thread().native_id:

warn = True

else:

# Fall back to Python-level Thread if native IDs are unavailable,

# mainly for PyPy.

if threading.current_thread() is not threading.main_thread():

warn = True

if warn:

_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 _get_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 _get_backend_mod().draw_if_interactive(*args, **kwargs)

@overload

def show(*, block: bool, **kwargs) -> None: ...

@overload

def show(*args: Any, **kwargs: Any) -> None: ...

# This function's signature is rewritten upon backend-load by switch_backend.

def show(*args, **kwargs) -> None:

"""

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 _get_backend_mod().show(*args, **kwargs)

def isinteractive() -> bool:

"""

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()

# Note: The return type of ioff being AbstractContextManager

# instead of ExitStack is deliberate.

# See https://github.com/matplotlib/matplotlib/issues/27659

# and https://github.com/matplotlib/matplotlib/pull/27667 for more info.

def ioff() -> AbstractContextManager:

"""

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 optional usage as a context manager, this function returns a

context manager object, which is not intended to be stored or

accessed by the user.

"""

stack = ExitStack()

stack.callback(ion if isinteractive() else ioff)

matplotlib.interactive(False)

uninstall_repl_displayhook()

return stack

# Note: The return type of ion being AbstractContextManager

# instead of ExitStack is deliberate.

# See https://github.com/matplotlib/matplotlib/issues/27659

# and https://github.com/matplotlib/matplotlib/pull/27667 for more info.

def ion() -> AbstractContextManager:

"""

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 optional usage as a context manager, this function returns a

context manager object, which is not intended to be stored or

accessed by the user.

"""

stack = ExitStack()

stack.callback(ion if isinteractive() else ioff)

matplotlib.interactive(True)

install_repl_displayhook()

return stack

def pause(interval: float) -> None:

"""

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: RcGroupKeyType, **kwargs) -> None:

matplotlib.rc(group, **kwargs)

@_copy_docstring_and_deprecators(matplotlib.rc_context)

def rc_context(

rc: dict[RcKeyType, Any] | None = None,

fname: str | pathlib.Path | os.PathLike | None = None,

) -> AbstractContextManager[None]:

return matplotlib.rc_context(rc, fname)

@_copy_docstring_and_deprecators(matplotlib.rcdefaults)

def rcdefaults() -> None:

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: float = 1, length: float = 100, randomness: float = 2

) -> ExitStack:

"""

Turn on `xkcd <https://xkcd.com/>`_ sketch-style drawing mode.

This will only have an effect on things drawn after this function is called.

For best results, install the `xkcd script <https://github.com/ipython/xkcd-font/>`_

font; xkcd fonts are not packaged 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, overriding those 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()

"""

# This cannot be implemented in terms of contextmanager() or rc_context()

# because this needs to work as a non-contextmanager too.

if rcParams['text.usetex']:

raise RuntimeError(

"xkcd mode is not compatible with text.usetex = True")

stack = ExitStack()

stack.callback(rcParams._update_raw, rcParams.copy())

from matplotlib import patheffects

rcParams.update({

'font.family': ['xkcd', 'xkcd Script', '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,

})

return stack

## Figures ##

def figure(

# autoincrement if None, else integer from 1-N

num: int | str | Figure | SubFigure | None = None,

# defaults to rc figure.figsize

figsize: ArrayLike # a 2-element ndarray is accepted as well

| tuple[float, float, Literal["in", "cm", "px"]]

| None = None,

# defaults to rc figure.dpi

dpi: float | None = None,

# defaults to rc figure.facecolor

facecolor: ColorType | None = None,

# defaults to rc figure.edgecolor

edgecolor: ColorType | None = None,

frameon: bool = True,

FigureClass: type[Figure] = Figure,

clear: bool = False,

**kwargs

) -> Figure:

"""

Create a new figure, or activate an existing figure.

Parameters

----------

num : int or str or `.Figure` or `.SubFigure`, 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. If num is a ``SubFigure``, its

parent ``Figure`` is activated.

If *num* is a Figure instance that is already tracked in pyplot, it is

activated. If *num* is a Figure instance that is not tracked in pyplot,

it is added to the tracked figures and activated.

figsize : (float, float) or (float, float, str), default: :rc:`figure.figsize`

The figure dimensions. This can be

- a tuple ``(width, height, unit)``, where *unit* is one of "in", "cm",

"px".

- a tuple ``(x, y)``, which is interpreted as ``(x, y, "in")``.

One of *width* or *height* may be ``None``; the respective value is taken

from :rc:`figure.figsize`.

dpi : float, default: :rc:`figure.dpi`

The resolution of the figure in dots-per-inch.

facecolor : :mpltype:`color`, default: :rc:`figure.facecolor`

The background color.

edgecolor : :mpltype:`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`

If set, an instance of this subclass will be created, rather than a

plain `.Figure`.

clear : bool, default: False

If True and the figure already exists, then it is cleared.

layout : {'constrained', 'compressed', 'tight', 'none', `.LayoutEngine`, None}, \

default: None

The layout mechanism for positioning of plot elements to avoid

overlapping Axes decorations (labels, ticks, etc). Note that layout

managers can measurably slow down figure display.

- 'constrained': The constrained layout solver adjusts Axes sizes

to avoid overlapping Axes decorations. Can handle complex plot

layouts and colorbars, and is thus recommended.

See :ref:`constrainedlayout_guide`

for examples.

- 'compressed': uses the same algorithm as 'constrained', but

removes extra space between fixed-aspect-ratio Axes. Best for

simple grids of Axes.

- 'tight': Use the tight layout mechanism. This is a relatively

simple algorithm that adjusts the subplot parameters so that

decorations do not overlap. See `.Figure.set_tight_layout` for

further details.

- 'none': Do not use a layout engine.

- A `.LayoutEngine` instance. Builtin layout classes are

`.ConstrainedLayoutEngine` and `.TightLayoutEngine`, more easily

View remainder of file in raw view

matplotlib/lib/matplotlib/pyplot.py at v3.11.2 · matplotlib/matplotlib · GitHub

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