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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">

<head>

<title>matplotlib.pyplot — Matplotlib 3.2.0 documentation</title>

title="Search within Matplotlib 3.2.0 documentation"

href="../../_static/opensearch.xml"/>

</head><body>

<div id="unreleased-message"> You are reading an old version of the documentation (v3.2.0). For the latest version see <a href="/stable/">https://matplotlib.org/stable/</a> </div>

<div style="float: left; position: absolute; width: 496px; bottom: 0; padding-bottom: 24px">Version 3.2.0</div>

<div id="forkongithub"><a href="https://github.com/matplotlib/matplotlib">Fork me on GitHub</a></div>

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<ul>

<li><a href="../../users/installing.html">Installation</a></li>

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<li><a href="../../gallery/index.html">Examples</a></li>

<li><a href="../../tutorials/index.html">Tutorials</a></li>

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<h3>Related Topics</h3>

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</div>

<h1>Source code for matplotlib.pyplot</h1><div class="highlight"><pre>

# 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

from cycler import cycler

import matplotlib

import matplotlib.colorbar

import matplotlib.image

from matplotlib import rcsetup, style

from matplotlib import _pylab_helpers, interactive

from matplotlib import cbook

from matplotlib.cbook import dedent, deprecated, silent_list, warn_deprecated

from matplotlib import docstring

from matplotlib.backend_bases import FigureCanvasBase

from matplotlib.figure import Figure, figaspect

from matplotlib.gridspec import GridSpec

from matplotlib import rcParams, rcParamsDefault, get_backend, rcParamsOrig

from matplotlib import rc_context

from matplotlib.rcsetup import interactive_bk as _interactive_bk

from matplotlib.artist import getp, get, Artist

from matplotlib.artist import setp as _setp

from matplotlib.axes import Axes, Subplot

from matplotlib.projections import PolarAxes

from matplotlib import mlab # for _csv2rec, detrend_none, window_hanning

from matplotlib.scale import get_scale_docs, 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

from matplotlib.backends import _get_running_interactive_framework

_log = logging.getLogger(__name__)

## 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

class _NotIPython(Exception):

pass

# see if we have IPython hooks around, if use them

try:

if 'IPython' in sys.modules:

from IPython import get_ipython

ip = get_ipython()

if ip is None:

raise _NotIPython()

if _IP_REGISTERED:

return

def post_execute():

if matplotlib.is_interactive():

draw_all()

# IPython >= 2

try:

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

# trigger IPython's eventloop integration, if available

from IPython.core.pylabtools import backend2gui

ipython_gui_name = backend2gui.get(get_backend())

if ipython_gui_name:

ip.enable_gui(ipython_gui_name)

else:

_INSTALL_FIG_OBSERVER = True

# import failed or ipython is not running

except (ImportError, _NotIPython):

_INSTALL_FIG_OBSERVER = True

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 it's 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:

raise NotImplementedError("Can not unregister events "

"in IPython < 2.0")

_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)

@docstring.copy(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 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.

"""

close("all")

if newbackend is rcsetup._auto_backend_sentinel:

# 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 [

"macosx", "qt5agg", "qt4agg", "gtk3agg", "tkagg", "wxagg"]:

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

backend_name = (

newbackend[9:] if newbackend.startswith("module://")

else "matplotlib.backends.backend_{}".format(newbackend.lower()))

backend_mod = importlib.import_module(backend_name)

Backend = type(

"Backend", (matplotlib.backends._Backend,), vars(backend_mod))

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

newbackend, Backend.backend_version)

required_framework = getattr(

Backend.FigureCanvas, "required_interactive_framework", None)

if required_framework is not None:

current_framework = \

matplotlib.backends._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))

rcParams['backend'] = rcParamsDefault['backend'] = newbackend

global _backend_mod, new_figure_manager, draw_if_interactive, _show

_backend_mod = backend_mod

new_figure_manager = Backend.new_figure_manager

draw_if_interactive = Backend.draw_if_interactive

_show = Backend.show

# 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 show(*args, **kw):

"""

Display all figures.

When running in ipython with its pylab mode, display all

figures and return to the ipython prompt.

In non-interactive mode, display all figures and block until

the figures have been closed; in interactive mode it has no

effect unless figures were created prior to a change from

non-interactive to interactive mode (not recommended). In

that case it displays the figures but does not block.

Parameters

----------

block : bool, optional

This is experimental, and may be set to ``True`` or ``False`` to

override the blocking behavior described above.

"""

global _show

return _show(*args, **kw)

def isinteractive():

"""Return whether to redraw after every plotting command."""

return matplotlib.is_interactive()

def ioff():

"""Turn the interactive mode off."""

matplotlib.interactive(False)

uninstall_repl_displayhook()

def ion():

"""Turn the interactive mode on."""

matplotlib.interactive(True)

install_repl_displayhook()

def pause(interval):

"""

Pause 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, see

:mod:`matplotlib.animation`.

Notes

-----

This function is experimental; its behavior may be changed or extended in a

future release.

"""

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)

@docstring.copy(matplotlib.rc)

def rc(group, **kwargs):

matplotlib.rc(group, **kwargs)

def rc_context(rc=None, fname=None):

return matplotlib.rc_context(rc, fname)

def rcdefaults():

matplotlib.rcdefaults()

if matplotlib.is_interactive():

draw_all()

## Current image ##

def gci():

"""

Get the current colorable artist.

Specifically, returns the current `.ScalarMappable` instance (`.Image`

created by `imshow` or `figimage`, `.Collection` created by `pcolor` or

`scatter`, etc.), or *None* if no such instance has been defined.

The current image is an attribute of the current axes, or the nearest

earlier axes in the current figure that contains an image.

Notes

-----

Historically, the only colorable artists were images; hence the name

``gci`` (get current image).

"""

return gcf()._gci()

## Any Artist ##

# (getp is simply imported)

@docstring.copy(_setp)

def setp(obj, *args, **kwargs):

return _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()

"""

if rcParams['text.usetex']:

raise RuntimeError(

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

from matplotlib import patheffects

return rc_context({

'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,

})

## 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.

Parameters

----------

num : int or str, optional, default: None

If not provided, a new figure will be created, and the figure number

will be incremented. The figure objects holds this number in a `number`

attribute.

If num is provided, and a figure with this id already exists, make

it active, and returns a reference to it. If this figure does not

exists, create it and returns it.

If num is a string, the window title will be set to this figure's

*num*.

figsize : (float, float), optional, default: None

width, height in inches. If not provided, defaults to

:rc:`figure.figsize` = ``[6.4, 4.8]``.

dpi : integer, optional, default: None

resolution of the figure. If not provided, defaults to

:rc:`figure.dpi` = ``100``.

facecolor : color

the background color. If not provided, defaults to

:rc:`figure.facecolor` = ``'w'``.

edgecolor : color

the border color. If not provided, defaults to

:rc:`figure.edgecolor` = ``'w'``.

frameon : bool, optional, default: True

If False, suppress drawing the figure frame.

FigureClass : subclass of `~matplotlib.figure.Figure`

Optionally use a custom `.Figure` instance.

clear : bool, optional, default: False

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

Returns

-------

figure : `~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 figsize is None:

figsize = rcParams['figure.figsize']

if dpi is None:

dpi = rcParams['figure.dpi']

if facecolor is None:

facecolor = rcParams['figure.facecolor']

if edgecolor is None:

edgecolor = rcParams['figure.edgecolor']

allnums = get_fignums()

next_num = max(allnums) + 1 if allnums else 1

figLabel = ''

if num is None:

num = next_num

elif isinstance(num, str):

figLabel = num

allLabels = get_figlabels()

if figLabel not in allLabels:

if figLabel == 'all':

cbook._warn_external(

"close('all') closes all existing figures")

num = next_num

else:

inum = allLabels.index(figLabel)

num = allnums[inum]

else:

num = int(num) # crude validation of num argument

figManager = _pylab_helpers.Gcf.get_fig_manager(num)

if figManager is None:

max_open_warning = rcParams['figure.max_open_warning']

if len(allnums) >= max_open_warning >= 1:

cbook._warn_external(

"More than %d figures have been opened. Figures "

"created through the pyplot interface "

"(`matplotlib.pyplot.figure`) are retained until "

"explicitly closed and may consume too much memory. "

"(To control this warning, see the rcParam "

"`figure.max_open_warning`)." %

max_open_warning, RuntimeWarning)

if get_backend().lower() == 'ps':

dpi = 72

figManager = new_figure_manager(num, figsize=figsize,

dpi=dpi,

facecolor=facecolor,

edgecolor=edgecolor,

frameon=frameon,

FigureClass=FigureClass,

**kwargs)

if figLabel:

figManager.set_window_title(figLabel)

figManager.canvas.figure.set_label(figLabel)

# make this figure current on button press event

def make_active(event):

_pylab_helpers.Gcf.set_active(figManager)

cid = figManager.canvas.mpl_connect('button_press_event', make_active)

figManager._cidgcf = cid

fig = figManager.canvas.figure

fig.number = num

# 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.

if matplotlib.is_interactive():

draw_if_interactive()

if _INSTALL_FIG_OBSERVER:

fig.stale_callback = _auto_draw_if_interactive

if clear:

return figManager.canvas.figure

def _auto_draw_if_interactive(fig, val):

"""

This is 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()

# 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()`.

"""

if figManager is not None:

return figManager.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."""

figManagers = _pylab_helpers.Gcf.get_all_fig_managers()

figManagers.sort(key=lambda m: m.num)

return [m.canvas.figure.get_label() for m in figManagers]

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 if no current figure exists, a new one is created an its figure

manager is returned.

Returns

-------

manager : `.FigureManagerBase` or backend-dependent subclass thereof

"""

return gcf().canvas.manager

@docstring.copy(FigureCanvasBase.mpl_connect)

def connect(s, func):

return gcf().canvas.mpl_connect(s, func)

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:

if figManager is None:

return

else:

_pylab_helpers.Gcf.destroy(figManager.num)

elif fig == 'all':

_pylab_helpers.Gcf.destroy_all()

elif isinstance(fig, int):

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):

allLabels = get_figlabels()

if fig in allLabels:

num = get_fignums()[allLabels.index(fig)]

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'")

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()

@docstring.copy(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(111, **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 : boolean, optional

If True, equivalent to projection='polar'.

sharex, sharey : `~.axes.Axes`, optional

Share the x or y `~matplotlib.axis` with sharex and/or sharey.

The axis will have the same limits, ticks, and scale as the axis

of the shared axes.

label : str

A label for the returned axes.

Other Parameters

----------------

**kwargs

This method also takes the keyword arguments for

the returned axes class. The keyword arguments for the

rectilinear axes class `~.axes.Axes` can be found in

the following table but there might also be other keyword

arguments if another projection is used, see the actual axes

class.

%(Axes)s

Returns

-------

axes : `~.axes.Axes` (or a subclass of `~.axes.Axes`)

The returned axes class depends on the projection used. It is

`~.axes.Axes` if rectilinear projection are used and

`.projections.polar.PolarAxes` if polar projection

are used.

Notes

-----

If the figure already has a axes with key (*args*,

*kwargs*) then it will simply make that axes current and

return it. This behavior is deprecated. Meanwhile, if you do

not want this behavior (i.e., you want to force the creation of a

new axes), you must use a unique set of args and kwargs. The axes

*label* attribute has been exposed for this purpose: if you want

two axes that are otherwise identical to be added to the figure,

make sure you give them unique labels.

See Also

--------

.Figure.add_axes

.pyplot.subplot

.Figure.add_subplot

.Figure.subplots

.pyplot.subplots

Examples

--------

::

# Creating a new full window axes

plt.axes()

# Creating a new axes with specified dimensions and some kwargs

plt.axes((left, bottom, width, height), facecolor='w')

"""

if arg is None:

return subplot(111, **kwargs)

else:

return gcf().add_axes(arg, **kwargs)

def delaxes(ax=None):

"""

Remove the `Axes` *ax* (defaulting to the current axes) from its figure.

A KeyError is raised if the axes doesn't exist.

"""

if ax is None:

ax = gca()

ax.figure.delaxes(ax)

def sca(ax):

"""

Set the current Axes instance to *ax*.

The current Figure is updated to the parent of *ax*.

"""

managers = _pylab_helpers.Gcf.get_all_fig_managers()

for m in managers:

if ax in m.canvas.figure.axes:

m.canvas.figure.sca(ax)

return

raise ValueError("Axes instance argument was not found in a figure")

def gca(**kwargs):

"""

Get the current :class:`~matplotlib.axes.Axes` instance on the

current figure matching the given keyword args, or create one.

Examples

--------

To get the current polar axes on the current figure::

plt.gca(projection='polar')

If the current axes doesn't exist, or isn't a polar one, the appropriate

axes will be created and then returned.

See Also

--------

matplotlib.figure.Figure.gca : The figure's gca method.

"""

return gcf().gca(**kwargs)

## More ways of creating axes ##

@docstring.dedent_interpd

def subplot(*args, **kwargs):

"""

Add a subplot to the current figure.

Wrapper of `.Figure.add_subplot` with a difference in behavior

explained in the notes section.

Call signatures::

subplot(nrows, ncols, index, **kwargs)

subplot(pos, **kwargs)

subplot(ax)

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