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nvim_config/typings/matplotlib/pyplot.pyi

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Initial configuration commit
1 year ago
"""
This type stub file was generated by pyright.
"""
import matplotlib
import matplotlib.image
import numpy as np
import datetime
import pathlib
import os
import PIL.Image
from contextlib import AbstractContextManager, ExitStack
from typing import Any, BinaryIO, Literal, TYPE_CHECKING, TypeVar, overload
from matplotlib import _api, _docstring, cbook, rcParams as rcParams, rcsetup
from matplotlib.backend_bases import Event, FigureCanvasBase, FigureManagerBase, MouseButton, RendererBase
from matplotlib.figure import Figure, SubFigure
from matplotlib.artist import Artist
from matplotlib.axes import Axes
from matplotlib.scale import ScaleBase
from matplotlib.cm import ScalarMappable
from matplotlib.colors import Colormap, Normalize
from collections.abc import Callable, Hashable, Iterable, Sequence
from typing_extensions import ParamSpec
from numpy.typing import ArrayLike
from matplotlib.axis import Tick
from matplotlib.axes._base import _AxesBase
from matplotlib.contour import ContourSet, QuadContourSet
from matplotlib.collections import BrokenBarHCollection, Collection, EventCollection, LineCollection, PathCollection, PolyCollection, QuadMesh
from matplotlib.colorbar import Colorbar
from matplotlib.container import BarContainer, ErrorbarContainer, StemContainer
from matplotlib.legend import Legend
from matplotlib.mlab import GaussianKDE
from matplotlib.image import AxesImage, FigureImage
from matplotlib.patches import FancyArrow, Polygon, StepPatch, Wedge
from matplotlib.quiver import Barbs, Quiver, QuiverKey
from matplotlib.transforms import Bbox, Transform
from matplotlib.typing import ColorType, HashableList, LineStyleType, MarkerType
from matplotlib.widgets import SubplotTool
from matplotlib.lines import Line2D
from matplotlib.text import Annotation, Text
"""
`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)
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)
See :ref:`api_interfaces` for an explanation of the tradeoffs between the
implicit and explicit interfaces.
"""
if TYPE_CHECKING:
_P = ParamSpec('_P')
_R = TypeVar('_R')
_T = TypeVar('_T')
_log = ...
colormaps = ...
color_sequences = ...
_ReplDisplayHook = ...
_REPL_DISPLAYHOOK = ...
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.
"""
...
def uninstall_repl_displayhook() -> None:
"""Disconnect from the display hook of the current shell."""
...
draw_all = ...
@_copy_docstring_and_deprecators(matplotlib.set_loglevel)
def set_loglevel(*args, **kwargs) -> None:
...
@_copy_docstring_and_deprecators(Artist.findobj)
def findobj(o: Artist | None = ..., match: Callable[[Artist], bool] | type[Artist] | None = ..., include_self: bool = ...) -> list[Artist]:
...
_backend_mod: type[matplotlib.backend_bases._Backend] | None = ...
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.
If the new backend is different than the current backend then all open
Figures will be closed via ``plt.close('all')``.
Parameters
----------
newbackend : str
The case-insensitive name of the backend to use.
"""
class backend_mod(matplotlib.backend_bases._Backend):
...
def new_figure_manager(*args, **kwargs):
"""Create a new figure manager instance."""
...
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.
"""
...
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.
"""
...
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.
"""
...
def ioff() -> ExitStack:
"""
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
`~contextlib.ExitStack` object, which is not intended to be stored or
accessed by the user.
"""
...
def ion() -> ExitStack:
"""
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
`~contextlib.ExitStack` object, which is not intended to be stored or
accessed by the user.
"""
...
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.
"""
...
@_copy_docstring_and_deprecators(matplotlib.rc)
def rc(group: str, **kwargs) -> None:
...
@_copy_docstring_and_deprecators(matplotlib.rc_context)
def rc_context(rc: dict[str, Any] | None = ..., fname: str | pathlib.Path | os.PathLike | None = ...) -> AbstractContextManager[None]:
...
@_copy_docstring_and_deprecators(matplotlib.rcdefaults)
def rcdefaults() -> None:
...
@_copy_docstring_and_deprecators(matplotlib.artist.getp)
def getp(obj, *args, **kwargs):
...
@_copy_docstring_and_deprecators(matplotlib.artist.get)
def get(obj, *args, **kwargs):
...
@_copy_docstring_and_deprecators(matplotlib.artist.setp)
def setp(obj, *args, **kwargs):
...
def xkcd(scale: float = ..., length: float = ..., randomness: float = ...) -> ExitStack:
"""
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()
"""
...
def figure(num: int | str | Figure | SubFigure | None = ..., figsize: tuple[float, float] | None = ..., dpi: float | None = ..., *, facecolor: ColorType | None = ..., edgecolor: ColorType | None = ..., frameon: bool = ..., FigureClass: type[Figure] = ..., clear: bool = ..., **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.
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`
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
accessible by 'constrained' and 'tight'. Passing an instance
allows third parties to provide their own layout engine.
If not given, fall back to using the parameters *tight_layout* and
*constrained_layout*, including their config defaults
:rc:`figure.autolayout` and :rc:`figure.constrained_layout.use`.
**kwargs
Additional keyword arguments are passed to the `.Figure` constructor.
Returns
-------
`~matplotlib.figure.Figure`
Notes
-----
A newly created figure is passed to the `~.FigureCanvasBase.new_manager`
method or the `new_figure_manager` function provided by the current
backend, which install a canvas and a manager on the figure.
Once this is done, :rc:`figure.hooks` are called, one at a time, on the
figure; these hooks allow arbitrary customization of the figure (e.g.,
attaching callbacks) or of associated elements (e.g., modifying the
toolbar). See :doc:`/gallery/user_interfaces/mplcvd` for an example of
toolbar customization.
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.
"""
...
def gcf() -> Figure:
"""
Get the current figure.
If there is currently no figure on the pyplot figure stack, a new one is
created using `~.pyplot.figure()`. (To test whether there is currently a
figure on the pyplot figure stack, check whether `~.pyplot.get_fignums()`
is empty.)
"""
...
def fignum_exists(num: int) -> bool:
"""Return whether the figure with the given id exists."""
...
def get_fignums() -> list[int]:
"""Return a list of existing figure numbers."""
...
def get_figlabels() -> list[Any]:
"""Return a list of existing figure labels."""
...
def get_current_fig_manager() -> FigureManagerBase | None:
"""
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
"""
...
@_copy_docstring_and_deprecators(FigureCanvasBase.mpl_connect)
def connect(s: str, func: Callable[[Event], Any]) -> int:
...
@_copy_docstring_and_deprecators(FigureCanvasBase.mpl_disconnect)
def disconnect(cid: int) -> None:
...
def close(fig: None | int | str | Figure | Literal["all"] = ...) -> 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
"""
...
def clf() -> None:
"""Clear the current figure."""
...
def draw() -> None:
"""
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.
See Also
--------
.FigureCanvasBase.draw_idle
.FigureCanvasBase.draw
"""
...
@_copy_docstring_and_deprecators(Figure.savefig)
def savefig(*args, **kwargs) -> None:
...
def figlegend(*args, **kwargs) -> Legend:
...
if Figure.legend.__doc__:
...
@_docstring.dedent_interpd
def axes(arg: None | tuple[float, float, float, float] = ..., **kwargs) -> matplotlib.axes.Axes:
"""
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'.
sharex, sharey : `~matplotlib.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.
Returns
-------
`~.axes.Axes`, or a subclass of `~.axes.Axes`
The returned axes class depends on the projection used. It is
`~.axes.Axes` if rectilinear projection is used and
`.projections.polar.PolarAxes` if polar projection is used.
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:kwdoc)s
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 a grey background
plt.axes((left, bottom, width, height), facecolor='grey')
"""
...
def delaxes(ax: matplotlib.axes.Axes | None = ...) -> None:
"""
Remove an `~.axes.Axes` (defaulting to the current axes) from its figure.
"""
...
def sca(ax: Axes) -> None:
"""
Set the current Axes to *ax* and the current Figure to the parent of *ax*.
"""
...
def cla() -> None:
"""Clear the current axes."""
...
@_docstring.dedent_interpd
def subplot(*args, **kwargs) -> Axes:
"""
Add an Axes to the current figure or retrieve an existing Axes.
This is a wrapper of `.Figure.add_subplot` which provides additional
behavior when working with the implicit API (see the notes section).
Call signatures::
subplot(nrows, ncols, index, **kwargs)
subplot(pos, **kwargs)
subplot(**kwargs)
subplot(ax)
Parameters
----------
*args : int, (int, int, *index*), or `.SubplotSpec`, default: (1, 1, 1)
The position of the subplot described by one of
- Three integers (*nrows*, *ncols*, *index*). The subplot will take the
*index* position on a grid with *nrows* rows and *ncols* columns.
*index* starts at 1 in the upper left corner and increases to the
right. *index* can also be a two-tuple specifying the (*first*,
*last*) indices (1-based, and including *last*) of the subplot, e.g.,
``fig.add_subplot(3, 1, (1, 2))`` makes a subplot that spans the
upper 2/3 of the figure.
- A 3-digit integer. The digits are interpreted as if given separately
as three single-digit integers, i.e. ``fig.add_subplot(235)`` is the
same as ``fig.add_subplot(2, 3, 5)``. Note that this can only be used
if there are no more than 9 subplots.
- A `.SubplotSpec`.
projection : {None, 'aitoff', 'hammer', 'lambert', 'mollweide', \
'polar', 'rectilinear', str}, optional
The projection type of the subplot (`~.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'.
sharex, sharey : `~matplotlib.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.
Returns
-------
`~.axes.Axes`
The Axes of the subplot. The returned Axes can actually be an instance
of a subclass, such as `.projections.polar.PolarAxes` for polar
projections.
Other Parameters
----------------
**kwargs
This method also takes the keyword arguments for the returned axes
base class; except for the *figure* argument. The keyword arguments
for the rectilinear base class `~.axes.Axes` can be found in
the following table but there might also be other keyword
arguments if another projection is used.
%(Axes:kwdoc)s
Notes
-----
Creating a new Axes will delete any preexisting Axes that
overlaps with it beyond sharing a boundary::
import matplotlib.pyplot as plt
# plot a line, implicitly creating a subplot(111)
plt.plot([1, 2, 3])
# now create a subplot which represents the top plot of a grid
# with 2 rows and 1 column. Since this subplot will overlap the
# first, the plot (and its axes) previously created, will be removed
plt.subplot(211)
If you do not want this behavior, use the `.Figure.add_subplot` method
or the `.pyplot.axes` function instead.
If no *kwargs* are passed and there exists an Axes in the location
specified by *args* then that Axes will be returned rather than a new
Axes being created.
If *kwargs* are passed and there exists an Axes in the location
specified by *args*, the projection type is the same, and the
*kwargs* match with the existing Axes, then the existing Axes is
returned. Otherwise a new Axes is created with the specified
parameters. We save a reference to the *kwargs* which we use
for this comparison. If any of the values in *kwargs* are
mutable we will not detect the case where they are mutated.
In these cases we suggest using `.Figure.add_subplot` and the
explicit Axes API rather than the implicit pyplot API.
See Also
--------
.Figure.add_subplot
.pyplot.subplots
.pyplot.axes
.Figure.subplots
Examples
--------
::
plt.subplot(221)
# equivalent but more general
ax1 = plt.subplot(2, 2, 1)
# add a subplot with no frame
ax2 = plt.subplot(222, frameon=False)
# add a polar subplot
plt.subplot(223, projection='polar')
# add a red subplot that shares the x-axis with ax1
plt.subplot(224, sharex=ax1, facecolor='red')
# delete ax2 from the figure
plt.delaxes(ax2)
# add ax2 to the figure again
plt.subplot(ax2)
# make the first axes "current" again
plt.subplot(221)
"""
...
def subplots(nrows: int = ..., ncols: int = ..., *, sharex: bool | Literal["none", "all", "row", "col"] = ..., sharey: bool | Literal["none", "all", "row", "col"] = ..., squeeze: bool = ..., width_ratios: Sequence[float] | None = ..., height_ratios: Sequence[float] | None = ..., subplot_kw: dict[str, Any] | None = ..., gridspec_kw: dict[str, Any] | None = ..., **fig_kw) -> tuple[Figure, Any]:
"""
Create a figure and a set of subplots.
This utility wrapper makes it convenient to create common layouts of
subplots, including the enclosing figure object, in a single call.
Parameters
----------
nrows, ncols : int, default: 1
Number of rows/columns of the subplot grid.
sharex, sharey : bool or {'none', 'all', 'row', 'col'}, default: False
Controls sharing of properties among x (*sharex*) or y (*sharey*)
axes:
- True or 'all': x- or y-axis will be shared among all subplots.
- False or 'none': each subplot x- or y-axis will be independent.
- 'row': each subplot row will share an x- or y-axis.
- 'col': each subplot column will share an x- or y-axis.
When subplots have a shared x-axis along a column, only the x tick
labels of the bottom subplot are created. Similarly, when subplots
have a shared y-axis along a row, only the y tick labels of the first
column subplot are created. To later turn other subplots' ticklabels
on, use `~matplotlib.axes.Axes.tick_params`.
When subplots have a shared axis that has units, calling
`~matplotlib.axis.Axis.set_units` will update each axis with the
new units.
squeeze : bool, default: True
- If True, extra dimensions are squeezed out from the returned
array of `~matplotlib.axes.Axes`:
- if only one subplot is constructed (nrows=ncols=1), the
resulting single Axes object is returned as a scalar.
- for Nx1 or 1xM subplots, the returned object is a 1D numpy
object array of Axes objects.
- for NxM, subplots with N>1 and M>1 are returned as a 2D array.
- If False, no squeezing at all is done: the returned Axes object is
always a 2D array containing Axes instances, even if it ends up
being 1x1.
width_ratios : array-like of length *ncols*, optional
Defines the relative widths of the columns. Each column gets a
relative width of ``width_ratios[i] / sum(width_ratios)``.
If not given, all columns will have the same width. Equivalent
to ``gridspec_kw={'width_ratios': [...]}``.
height_ratios : array-like of length *nrows*, optional
Defines the relative heights of the rows. Each row gets a
relative height of ``height_ratios[i] / sum(height_ratios)``.
If not given, all rows will have the same height. Convenience
for ``gridspec_kw={'height_ratios': [...]}``.
subplot_kw : dict, optional
Dict with keywords passed to the
`~matplotlib.figure.Figure.add_subplot` call used to create each
subplot.
gridspec_kw : dict, optional
Dict with keywords passed to the `~matplotlib.gridspec.GridSpec`
constructor used to create the grid the subplots are placed on.
**fig_kw
All additional keyword arguments are passed to the
`.pyplot.figure` call.
Returns
-------
fig : `.Figure`
ax : `~matplotlib.axes.Axes` or array of Axes
*ax* can be either a single `~.axes.Axes` object, or an array of Axes
objects if more than one subplot was created. The dimensions of the
resulting array can be controlled with the squeeze keyword, see above.
Typical idioms for handling the return value are::
# using the variable ax for single a Axes
fig, ax = plt.subplots()
# using the variable axs for multiple Axes
fig, axs = plt.subplots(2, 2)
# using tuple unpacking for multiple Axes
fig, (ax1, ax2) = plt.subplots(1, 2)
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
The names ``ax`` and pluralized ``axs`` are preferred over ``axes``
because for the latter it's not clear if it refers to a single
`~.axes.Axes` instance or a collection of these.
See Also
--------
.pyplot.figure
.pyplot.subplot
.pyplot.axes
.Figure.subplots
.Figure.add_subplot
Examples
--------
::
# First create some toy data:
x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)
# Create just a figure and only one subplot
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_title('Simple plot')
# Create two subplots and unpack the output array immediately
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.plot(x, y)
ax1.set_title('Sharing Y axis')
ax2.scatter(x, y)
# Create four polar axes and access them through the returned array
fig, axs = plt.subplots(2, 2, subplot_kw=dict(projection="polar"))
axs[0, 0].plot(x, y)
axs[1, 1].scatter(x, y)
# Share a X axis with each column of subplots
plt.subplots(2, 2, sharex='col')
# Share a Y axis with each row of subplots
plt.subplots(2, 2, sharey='row')
# Share both X and Y axes with all subplots
plt.subplots(2, 2, sharex='all', sharey='all')
# Note that this is the same as
plt.subplots(2, 2, sharex=True, sharey=True)
# Create figure number 10 with a single subplot
# and clears it if it already exists.
fig, ax = plt.subplots(num=10, clear=True)
"""
...
@overload
def subplot_mosaic(mosaic: str, *, sharex: bool = ..., sharey: bool = ..., width_ratios: ArrayLike | None = ..., height_ratios: ArrayLike | None = ..., empty_sentinel: str = ..., subplot_kw: dict[str, Any] | None = ..., gridspec_kw: dict[str, Any] | None = ..., per_subplot_kw: dict[str | tuple[str, ...], dict[str, Any]] | None = ..., **fig_kw: Any) -> tuple[Figure, dict[str, matplotlib.axes.Axes]]:
...
@overload
def subplot_mosaic(mosaic: list[HashableList[_T]], *, sharex: bool = ..., sharey: bool = ..., width_ratios: ArrayLike | None = ..., height_ratios: ArrayLike | None = ..., empty_sentinel: _T = ..., subplot_kw: dict[str, Any] | None = ..., gridspec_kw: dict[str, Any] | None = ..., per_subplot_kw: dict[_T | tuple[_T, ...], dict[str, Any]] | None = ..., **fig_kw: Any) -> tuple[Figure, dict[_T, matplotlib.axes.Axes]]:
...
@overload
def subplot_mosaic(mosaic: list[HashableList[Hashable]], *, sharex: bool = ..., sharey: bool = ..., width_ratios: ArrayLike | None = ..., height_ratios: ArrayLike | None = ..., empty_sentinel: Any = ..., subplot_kw: dict[str, Any] | None = ..., gridspec_kw: dict[str, Any] | None = ..., per_subplot_kw: dict[Hashable | tuple[Hashable, ...], dict[str, Any]] | None = ..., **fig_kw: Any) -> tuple[Figure, dict[Hashable, matplotlib.axes.Axes]]:
...
def subplot_mosaic(mosaic: str | list[HashableList[_T]] | list[HashableList[Hashable]], *, sharex: bool = ..., sharey: bool = ..., width_ratios: ArrayLike | None = ..., height_ratios: ArrayLike | None = ..., empty_sentinel: Any = ..., subplot_kw: dict[str, Any] | None = ..., gridspec_kw: dict[str, Any] | None = ..., per_subplot_kw: dict[str | tuple[str, ...], dict[str, Any]] | dict[_T | tuple[_T, ...], dict[str, Any]] | dict[Hashable | tuple[Hashable, ...], dict[str, Any]] | None = ..., **fig_kw: Any) -> tuple[Figure, dict[str, matplotlib.axes.Axes]] | tuple[Figure, dict[_T, matplotlib.axes.Axes]] | tuple[Figure, dict[Hashable, matplotlib.axes.Axes]]:
"""
Build a layout of Axes based on ASCII art or nested lists.
This is a helper function to build complex GridSpec layouts visually.
See :ref:`mosaic`
for an example and full API documentation
Parameters
----------
mosaic : list of list of {hashable or nested} or str
A visual layout of how you want your Axes to be arranged
labeled as strings. For example ::
x = [['A panel', 'A panel', 'edge'],
['C panel', '.', 'edge']]
produces 4 axes:
- 'A panel' which is 1 row high and spans the first two columns
- 'edge' which is 2 rows high and is on the right edge
- 'C panel' which in 1 row and 1 column wide in the bottom left
- a blank space 1 row and 1 column wide in the bottom center
Any of the entries in the layout can be a list of lists
of the same form to create nested layouts.
If input is a str, then it must be of the form ::
'''
AAE
C.E
'''
where each character is a column and each line is a row.
This only allows only single character Axes labels and does
not allow nesting but is very terse.
sharex, sharey : bool, default: False
If True, the x-axis (*sharex*) or y-axis (*sharey*) will be shared
among all subplots. In that case, tick label visibility and axis units
behave as for `subplots`. If False, each subplot's x- or y-axis will
be independent.
width_ratios : array-like of length *ncols*, optional
Defines the relative widths of the columns. Each column gets a
relative width of ``width_ratios[i] / sum(width_ratios)``.
If not given, all columns will have the same width. Convenience
for ``gridspec_kw={'width_ratios': [...]}``.
height_ratios : array-like of length *nrows*, optional
Defines the relative heights of the rows. Each row gets a
relative height of ``height_ratios[i] / sum(height_ratios)``.
If not given, all rows will have the same height. Convenience
for ``gridspec_kw={'height_ratios': [...]}``.
empty_sentinel : object, optional
Entry in the layout to mean "leave this space empty". Defaults
to ``'.'``. Note, if *layout* is a string, it is processed via
`inspect.cleandoc` to remove leading white space, which may
interfere with using white-space as the empty sentinel.
subplot_kw : dict, optional
Dictionary with keywords passed to the `.Figure.add_subplot` call
used to create each subplot. These values may be overridden by
values in *per_subplot_kw*.
per_subplot_kw : dict, optional
A dictionary mapping the Axes identifiers or tuples of identifiers
to a dictionary of keyword arguments to be passed to the
`.Figure.add_subplot` call used to create each subplot. The values
in these dictionaries have precedence over the values in
*subplot_kw*.
If *mosaic* is a string, and thus all keys are single characters,
it is possible to use a single string instead of a tuple as keys;
i.e. ``"AB"`` is equivalent to ``("A", "B")``.
.. versionadded:: 3.7
gridspec_kw : dict, optional
Dictionary with keywords passed to the `.GridSpec` constructor used
to create the grid the subplots are placed on.
**fig_kw
All additional keyword arguments are passed to the
`.pyplot.figure` call.
Returns
-------
fig : `.Figure`
The new figure
dict[label, Axes]
A dictionary mapping the labels to the Axes objects. The order of
the axes is left-to-right and top-to-bottom of their position in the
total layout.
"""
...
def subplot2grid(shape: tuple[int, int], loc: tuple[int, int], rowspan: int = ..., colspan: int = ..., fig: Figure | None = ..., **kwargs) -> matplotlib.axes.Axes:
"""
Create a subplot at a specific location inside a regular grid.
Parameters
----------
shape : (int, int)
Number of rows and of columns of the grid in which to place axis.
loc : (int, int)
Row number and column number of the axis location within the grid.
rowspan : int, default: 1
Number of rows for the axis to span downwards.
colspan : int, default: 1
Number of columns for the axis to span to the right.
fig : `.Figure`, optional
Figure to place the subplot in. Defaults to the current figure.
**kwargs
Additional keyword arguments are handed to `~.Figure.add_subplot`.
Returns
-------
`~.axes.Axes`
The Axes of the subplot. The returned Axes can actually be an instance
of a subclass, such as `.projections.polar.PolarAxes` for polar
projections.
Notes
-----
The following call ::
ax = subplot2grid((nrows, ncols), (row, col), rowspan, colspan)
is identical to ::
fig = gcf()
gs = fig.add_gridspec(nrows, ncols)
ax = fig.add_subplot(gs[row:row+rowspan, col:col+colspan])
"""
...
def twinx(ax: matplotlib.axes.Axes | None = ...) -> _AxesBase:
"""
Make and return a second axes that shares the *x*-axis. The new axes will
overlay *ax* (or the current axes if *ax* is *None*), and its ticks will be
on the right.
Examples
--------
:doc:`/gallery/subplots_axes_and_figures/two_scales`
"""
...
def twiny(ax: matplotlib.axes.Axes | None = ...) -> _AxesBase:
"""
Make and return a second axes that shares the *y*-axis. The new axes will
overlay *ax* (or the current axes if *ax* is *None*), and its ticks will be
on the top.
Examples
--------
:doc:`/gallery/subplots_axes_and_figures/two_scales`
"""
...
def subplot_tool(targetfig: Figure | None = ...) -> SubplotTool | None:
"""
Launch a subplot tool window for a figure.
Returns
-------
`matplotlib.widgets.SubplotTool`
"""
...
def box(on: bool | None = ...) -> None:
"""
Turn the axes box on or off on the current axes.
Parameters
----------
on : bool or None
The new `~matplotlib.axes.Axes` box state. If ``None``, toggle
the state.
See Also
--------
:meth:`matplotlib.axes.Axes.set_frame_on`
:meth:`matplotlib.axes.Axes.get_frame_on`
"""
...
def xlim(*args, **kwargs) -> tuple[float, float]:
"""
Get or set the x limits of the current axes.
Call signatures::
left, right = xlim() # return the current xlim
xlim((left, right)) # set the xlim to left, right
xlim(left, right) # set the xlim to left, right
If you do not specify args, you can pass *left* or *right* as kwargs,
i.e.::
xlim(right=3) # adjust the right leaving left unchanged
xlim(left=1) # adjust the left leaving right unchanged
Setting limits turns autoscaling off for the x-axis.
Returns
-------
left, right
A tuple of the new x-axis limits.
Notes
-----
Calling this function with no arguments (e.g. ``xlim()``) is the pyplot
equivalent of calling `~.Axes.get_xlim` on the current axes.
Calling this function with arguments is the pyplot equivalent of calling
`~.Axes.set_xlim` on the current axes. All arguments are passed though.
"""
...
def ylim(*args, **kwargs) -> tuple[float, float]:
"""
Get or set the y-limits of the current axes.
Call signatures::
bottom, top = ylim() # return the current ylim
ylim((bottom, top)) # set the ylim to bottom, top
ylim(bottom, top) # set the ylim to bottom, top
If you do not specify args, you can alternatively pass *bottom* or
*top* as kwargs, i.e.::
ylim(top=3) # adjust the top leaving bottom unchanged
ylim(bottom=1) # adjust the bottom leaving top unchanged
Setting limits turns autoscaling off for the y-axis.
Returns
-------
bottom, top
A tuple of the new y-axis limits.
Notes
-----
Calling this function with no arguments (e.g. ``ylim()``) is the pyplot
equivalent of calling `~.Axes.get_ylim` on the current axes.
Calling this function with arguments is the pyplot equivalent of calling
`~.Axes.set_ylim` on the current axes. All arguments are passed though.
"""
...
def xticks(ticks: ArrayLike | None = ..., labels: Sequence[str] | None = ..., *, minor: bool = ..., **kwargs) -> tuple[list[Tick] | np.ndarray, list[Text]]:
"""
Get or set the current tick locations and labels of the x-axis.
Pass no arguments to return the current values without modifying them.
Parameters
----------
ticks : array-like, optional
The list of xtick locations. Passing an empty list removes all xticks.
labels : array-like, optional
The labels to place at the given *ticks* locations. This argument can
only be passed if *ticks* is passed as well.
minor : bool, default: False
If ``False``, get/set the major ticks/labels; if ``True``, the minor
ticks/labels.
**kwargs
`.Text` properties can be used to control the appearance of the labels.
Returns
-------
locs
The list of xtick locations.
labels
The list of xlabel `.Text` objects.
Notes
-----
Calling this function with no arguments (e.g. ``xticks()``) is the pyplot
equivalent of calling `~.Axes.get_xticks` and `~.Axes.get_xticklabels` on
the current axes.
Calling this function with arguments is the pyplot equivalent of calling
`~.Axes.set_xticks` and `~.Axes.set_xticklabels` on the current axes.
Examples
--------
>>> locs, labels = xticks() # Get the current locations and labels.
>>> xticks(np.arange(0, 1, step=0.2)) # Set label locations.
>>> xticks(np.arange(3), ['Tom', 'Dick', 'Sue']) # Set text labels.
>>> xticks([0, 1, 2], ['January', 'February', 'March'],
... rotation=20) # Set text labels and properties.
>>> xticks([]) # Disable xticks.
"""
...
def yticks(ticks: ArrayLike | None = ..., labels: Sequence[str] | None = ..., *, minor: bool = ..., **kwargs) -> tuple[list[Tick] | np.ndarray, list[Text]]:
"""
Get or set the current tick locations and labels of the y-axis.
Pass no arguments to return the current values without modifying them.
Parameters
----------
ticks : array-like, optional
The list of ytick locations. Passing an empty list removes all yticks.
labels : array-like, optional
The labels to place at the given *ticks* locations. This argument can
only be passed if *ticks* is passed as well.
minor : bool, default: False
If ``False``, get/set the major ticks/labels; if ``True``, the minor
ticks/labels.
**kwargs
`.Text` properties can be used to control the appearance of the labels.
Returns
-------
locs
The list of ytick locations.
labels
The list of ylabel `.Text` objects.
Notes
-----
Calling this function with no arguments (e.g. ``yticks()``) is the pyplot
equivalent of calling `~.Axes.get_yticks` and `~.Axes.get_yticklabels` on
the current axes.
Calling this function with arguments is the pyplot equivalent of calling
`~.Axes.set_yticks` and `~.Axes.set_yticklabels` on the current axes.
Examples
--------
>>> locs, labels = yticks() # Get the current locations and labels.
>>> yticks(np.arange(0, 1, step=0.2)) # Set label locations.
>>> yticks(np.arange(3), ['Tom', 'Dick', 'Sue']) # Set text labels.
>>> yticks([0, 1, 2], ['January', 'February', 'March'],
... rotation=45) # Set text labels and properties.
>>> yticks([]) # Disable yticks.
"""
...
def rgrids(radii: ArrayLike | None = ..., labels: Sequence[str | Text] | None = ..., angle: float | None = ..., fmt: str | None = ..., **kwargs) -> tuple[list[Line2D], list[Text]]:
"""
Get or set the radial gridlines on the current polar plot.
Call signatures::
lines, labels = rgrids()
lines, labels = rgrids(radii, labels=None, angle=22.5, fmt=None, **kwargs)
When called with no arguments, `.rgrids` simply returns the tuple
(*lines*, *labels*). When called with arguments, the labels will
appear at the specified radial distances and angle.
Parameters
----------
radii : tuple with floats
The radii for the radial gridlines
labels : tuple with strings or None
The labels to use at each radial gridline. The
`matplotlib.ticker.ScalarFormatter` will be used if None.
angle : float
The angular position of the radius labels in degrees.
fmt : str or None
Format string used in `matplotlib.ticker.FormatStrFormatter`.
For example '%f'.
Returns
-------
lines : list of `.lines.Line2D`
The radial gridlines.
labels : list of `.text.Text`
The tick labels.
Other Parameters
----------------
**kwargs
*kwargs* are optional `.Text` properties for the labels.
See Also
--------
.pyplot.thetagrids
.projections.polar.PolarAxes.set_rgrids
.Axis.get_gridlines
.Axis.get_ticklabels
Examples
--------
::
# set the locations of the radial gridlines
lines, labels = rgrids( (0.25, 0.5, 1.0) )
# set the locations and labels of the radial gridlines
lines, labels = rgrids( (0.25, 0.5, 1.0), ('Tom', 'Dick', 'Harry' ))
"""
...
def thetagrids(angles: ArrayLike | None = ..., labels: Sequence[str | Text] | None = ..., fmt: str | None = ..., **kwargs) -> tuple[list[Line2D], list[Text]]:
"""
Get or set the theta gridlines on the current polar plot.
Call signatures::
lines, labels = thetagrids()
lines, labels = thetagrids(angles, labels=None, fmt=None, **kwargs)
When called with no arguments, `.thetagrids` simply returns the tuple
(*lines*, *labels*). When called with arguments, the labels will
appear at the specified angles.
Parameters
----------
angles : tuple with floats, degrees
The angles of the theta gridlines.
labels : tuple with strings or None
The labels to use at each radial gridline. The
`.projections.polar.ThetaFormatter` will be used if None.
fmt : str or None
Format string used in `matplotlib.ticker.FormatStrFormatter`.
For example '%f'. Note that the angle in radians will be used.
Returns
-------
lines : list of `.lines.Line2D`
The theta gridlines.
labels : list of `.text.Text`
The tick labels.
Other Parameters
----------------
**kwargs
*kwargs* are optional `.Text` properties for the labels.
See Also
--------
.pyplot.rgrids
.projections.polar.PolarAxes.set_thetagrids
.Axis.get_gridlines
.Axis.get_ticklabels
Examples
--------
::
# set the locations of the angular gridlines
lines, labels = thetagrids(range(45, 360, 90))
# set the locations and labels of the angular gridlines
lines, labels = thetagrids(range(45, 360, 90), ('NE', 'NW', 'SW', 'SE'))
"""
...
@_api.deprecated("3.7", pending=True)
def get_plot_commands() -> list[str]:
"""
Get a sorted list of all of the plotting commands.
"""
...
@_copy_docstring_and_deprecators(Figure.colorbar)
def colorbar(mappable: ScalarMappable | None = ..., cax: matplotlib.axes.Axes | None = ..., ax: matplotlib.axes.Axes | Iterable[matplotlib.axes.Axes] | None = ..., **kwargs) -> Colorbar:
...
def clim(vmin: float | None = ..., vmax: float | None = ...) -> None:
"""
Set the color limits of the current image.
If either *vmin* or *vmax* is None, the image min/max respectively
will be used for color scaling.
If you want to set the clim of multiple images, use
`~.ScalarMappable.set_clim` on every image, for example::
for im in gca().get_images():
im.set_clim(0, 0.5)
"""
...
def get_cmap(name: Colormap | str | None = ..., lut: int | None = ...) -> Colormap:
...
def set_cmap(cmap: Colormap | str) -> None:
"""
Set the default colormap, and applies it to the current image if any.
Parameters
----------
cmap : `~matplotlib.colors.Colormap` or str
A colormap instance or the name of a registered colormap.
See Also
--------
colormaps
matplotlib.cm.register_cmap
matplotlib.cm.get_cmap
"""
...
@_copy_docstring_and_deprecators(matplotlib.image.imread)
def imread(fname: str | pathlib.Path | BinaryIO, format: str | None = ...) -> np.ndarray:
...
@_copy_docstring_and_deprecators(matplotlib.image.imsave)
def imsave(fname: str | os.PathLike | BinaryIO, arr: ArrayLike, **kwargs) -> None:
...
def matshow(A: ArrayLike, fignum: None | int = ..., **kwargs) -> AxesImage:
"""
Display an array as a matrix in a new figure window.
The origin is set at the upper left hand corner and rows (first
dimension of the array) are displayed horizontally. The aspect
ratio of the figure window is that of the array, unless this would
make an excessively short or narrow figure.
Tick labels for the xaxis are placed on top.
Parameters
----------
A : 2D array-like
The matrix to be displayed.
fignum : None or int
If *None*, create a new figure window with automatic numbering.
If a nonzero integer, draw into the figure with the given number
(create it if it does not exist).
If 0, use the current axes (or create one if it does not exist).
.. note::
Because of how `.Axes.matshow` tries to set the figure aspect
ratio to be the one of the array, strange things may happen if you
reuse an existing figure.
Returns
-------
`~matplotlib.image.AxesImage`
Other Parameters
----------------
**kwargs : `~matplotlib.axes.Axes.imshow` arguments
"""
...
def polar(*args, **kwargs) -> list[Line2D]:
"""
Make a polar plot.
call signature::
polar(theta, r, **kwargs)
Multiple *theta*, *r* arguments are supported, with format strings, as in
`plot`.
"""
...
if (rcParams["backend_fallback"] and rcParams._get_backend_or_none() in (set(rcsetup.interactive_bk) - 'WebAgg', 'nbAgg') and cbook._get_running_interactive_framework()):
...
@_copy_docstring_and_deprecators(Figure.figimage)
def figimage(X: ArrayLike, xo: int = ..., yo: int = ..., alpha: float | None = ..., norm: str | Normalize | None = ..., cmap: str | Colormap | None = ..., vmin: float | None = ..., vmax: float | None = ..., origin: Literal["upper", "lower"] | None = ..., resize: bool = ..., **kwargs) -> FigureImage:
...
@_copy_docstring_and_deprecators(Figure.text)
def figtext(x: float, y: float, s: str, fontdict: dict[str, Any] | None = ..., **kwargs) -> Text:
...
@_copy_docstring_and_deprecators(Figure.gca)
def gca() -> Axes:
...
@_copy_docstring_and_deprecators(Figure._gci)
def gci() -> ScalarMappable | None:
...
@_copy_docstring_and_deprecators(Figure.ginput)
def ginput(n: int = ..., timeout: float = ..., show_clicks: bool = ..., mouse_add: MouseButton = ..., mouse_pop: MouseButton = ..., mouse_stop: MouseButton = ...) -> list[tuple[int, int]]:
...
@_copy_docstring_and_deprecators(Figure.subplots_adjust)
def subplots_adjust(left: float | None = ..., bottom: float | None = ..., right: float | None = ..., top: float | None = ..., wspace: float | None = ..., hspace: float | None = ...) -> None:
...
@_copy_docstring_and_deprecators(Figure.suptitle)
def suptitle(t: str, **kwargs) -> Text:
...
@_copy_docstring_and_deprecators(Figure.tight_layout)
def tight_layout(*, pad: float = ..., h_pad: float | None = ..., w_pad: float | None = ..., rect: tuple[float, float, float, float] | None = ...) -> None:
...
@_copy_docstring_and_deprecators(Figure.waitforbuttonpress)
def waitforbuttonpress(timeout: float = ...) -> None | bool:
...
@_copy_docstring_and_deprecators(Axes.acorr)
def acorr(x: ArrayLike, *, data=..., **kwargs) -> tuple[np.ndarray, np.ndarray, LineCollection | Line2D, Line2D | None]:
...
@_copy_docstring_and_deprecators(Axes.angle_spectrum)
def angle_spectrum(x: ArrayLike, Fs: float | None = ..., Fc: int | None = ..., window: Callable[[ArrayLike], ArrayLike] | ArrayLike | None = ..., pad_to: int | None = ..., sides: Literal["default", "onesided", "twosided"] | None = ..., *, data=..., **kwargs) -> tuple[np.ndarray, np.ndarray, Line2D]:
...
@_copy_docstring_and_deprecators(Axes.annotate)
def annotate(text: str, xy: tuple[float, float], xytext: tuple[float, float] | None = ..., xycoords: str | Artist | Transform | Callable[[RendererBase], Bbox | Transform] | tuple[float, float] = ..., textcoords: str | Artist | Transform | Callable[[RendererBase], Bbox | Transform] | tuple[float, float] | None = ..., arrowprops: dict[str, Any] | None = ..., annotation_clip: bool | None = ..., **kwargs) -> Annotation:
...
@_copy_docstring_and_deprecators(Axes.arrow)
def arrow(x: float, y: float, dx: float, dy: float, **kwargs) -> FancyArrow:
...
@_copy_docstring_and_deprecators(Axes.autoscale)
def autoscale(enable: bool = ..., axis: Literal["both", "x", "y"] = ..., tight: bool | None = ...) -> None:
...
@_copy_docstring_and_deprecators(Axes.axhline)
def axhline(y: float = ..., xmin: float = ..., xmax: float = ..., **kwargs) -> Line2D:
...
@_copy_docstring_and_deprecators(Axes.axhspan)
def axhspan(ymin: float, ymax: float, xmin: float = ..., xmax: float = ..., **kwargs) -> Polygon:
...
@_copy_docstring_and_deprecators(Axes.axis)
def axis(arg: tuple[float, float, float, float] | bool | str | None = ..., /, *, emit: bool = ..., **kwargs) -> tuple[float, float, float, float]:
...
@_copy_docstring_and_deprecators(Axes.axline)
def axline(xy1: tuple[float, float], xy2: tuple[float, float] | None = ..., *, slope: float | None = ..., **kwargs) -> Line2D:
...
@_copy_docstring_and_deprecators(Axes.axvline)
def axvline(x: float = ..., ymin: float = ..., ymax: float = ..., **kwargs) -> Line2D:
...
@_copy_docstring_and_deprecators(Axes.axvspan)
def axvspan(xmin: float, xmax: float, ymin: float = ..., ymax: float = ..., **kwargs) -> Polygon:
...
@_copy_docstring_and_deprecators(Axes.bar)
def bar(x: float | ArrayLike, height: float | ArrayLike, width: float | ArrayLike = ..., bottom: float | ArrayLike | None = ..., *, align: Literal["center", "edge"] = ..., data=..., **kwargs) -> BarContainer:
...
@_copy_docstring_and_deprecators(Axes.barbs)
def barbs(*args, data=..., **kwargs) -> Barbs:
...
@_copy_docstring_and_deprecators(Axes.barh)
def barh(y: float | ArrayLike, width: float | ArrayLike, height: float | ArrayLike = ..., left: float | ArrayLike | None = ..., *, align: Literal["center", "edge"] = ..., data=..., **kwargs) -> BarContainer:
...
@_copy_docstring_and_deprecators(Axes.bar_label)
def bar_label(container: BarContainer, labels: ArrayLike | None = ..., *, fmt: str | Callable[[float], str] = ..., label_type: Literal["center", "edge"] = ..., padding: float = ..., **kwargs) -> list[Annotation]:
...
@_copy_docstring_and_deprecators(Axes.boxplot)
def boxplot(x: ArrayLike | Sequence[ArrayLike], notch: bool | None = ..., sym: str | None = ..., vert: bool | None = ..., whis: float | tuple[float, float] | None = ..., positions: ArrayLike | None = ..., widths: float | ArrayLike | None = ..., patch_artist: bool | None = ..., bootstrap: int | None = ..., usermedians: ArrayLike | None = ..., conf_intervals: ArrayLike | None = ..., meanline: bool | None = ..., showmeans: bool | None = ..., showcaps: bool | None = ..., showbox: bool | None = ..., showfliers: bool | None = ..., boxprops: dict[str, Any] | None = ..., labels: Sequence[str] | None = ..., flierprops: dict[str, Any] | None = ..., medianprops: dict[str, Any] | None = ..., meanprops: dict[str, Any] | None = ..., capprops: dict[str, Any] | None = ..., whiskerprops: dict[str, Any] | None = ..., manage_ticks: bool = ..., autorange: bool = ..., zorder: float | None = ..., capwidths: float | ArrayLike | None = ..., *, data=...) -> dict[str, Any]:
...
@_copy_docstring_and_deprecators(Axes.broken_barh)
def broken_barh(xranges: Sequence[tuple[float, float]], yrange: tuple[float, float], *, data=..., **kwargs) -> BrokenBarHCollection:
...
@_copy_docstring_and_deprecators(Axes.clabel)
def clabel(CS: ContourSet, levels: ArrayLike | None = ..., **kwargs) -> list[Text]:
...
@_copy_docstring_and_deprecators(Axes.cohere)
def cohere(x: ArrayLike, y: ArrayLike, NFFT: int = ..., Fs: float = ..., Fc: int = ..., detrend: Literal["none", "mean", "linear"] | Callable[[ArrayLike], ArrayLike] = ..., window: Callable[[ArrayLike], ArrayLike] | ArrayLike = ..., noverlap: int = ..., pad_to: int | None = ..., sides: Literal["default", "onesided", "twosided"] = ..., scale_by_freq: bool | None = ..., *, data=..., **kwargs) -> tuple[np.ndarray, np.ndarray]:
...
@_copy_docstring_and_deprecators(Axes.contour)
def contour(*args, data=..., **kwargs) -> QuadContourSet:
...
@_copy_docstring_and_deprecators(Axes.contourf)
def contourf(*args, data=..., **kwargs) -> QuadContourSet:
...
@_copy_docstring_and_deprecators(Axes.csd)
def csd(x: ArrayLike, y: ArrayLike, NFFT: int | None = ..., Fs: float | None = ..., Fc: int | None = ..., detrend: Literal["none", "mean", "linear"] | Callable[[ArrayLike], ArrayLike] | None = ..., window: Callable[[ArrayLike], ArrayLike] | ArrayLike | None = ..., noverlap: int | None = ..., pad_to: int | None = ..., sides: Literal["default", "onesided", "twosided"] | None = ..., scale_by_freq: bool | None = ..., return_line: bool | None = ..., *, data=..., **kwargs) -> tuple[np.ndarray, np.ndarray] | tuple[np.ndarray, np.ndarray, Line2D]:
...
@_copy_docstring_and_deprecators(Axes.ecdf)
def ecdf(x: ArrayLike, weights: ArrayLike | None = ..., *, complementary: bool = ..., orientation: Literal["vertical", "horizonatal"] = ..., compress: bool = ..., data=..., **kwargs) -> Line2D:
...
@_copy_docstring_and_deprecators(Axes.errorbar)
def errorbar(x: float | ArrayLike, y: float | ArrayLike, yerr: float | ArrayLike | None = ..., xerr: float | ArrayLike | None = ..., fmt: str = ..., ecolor: ColorType | None = ..., elinewidth: float | None = ..., capsize: float | None = ..., barsabove: bool = ..., lolims: bool | ArrayLike = ..., uplims: bool | ArrayLike = ..., xlolims: bool | ArrayLike = ..., xuplims: bool | ArrayLike = ..., errorevery: int | tuple[int, int] = ..., capthick: float | None = ..., *, data=..., **kwargs) -> ErrorbarContainer:
...
@_copy_docstring_and_deprecators(Axes.eventplot)
def eventplot(positions: ArrayLike | Sequence[ArrayLike], orientation: Literal["horizontal", "vertical"] = ..., lineoffsets: float | Sequence[float] = ..., linelengths: float | Sequence[float] = ..., linewidths: float | Sequence[float] | None = ..., colors: ColorType | Sequence[ColorType] | None = ..., alpha: float | Sequence[float] | None = ..., linestyles: LineStyleType | Sequence[LineStyleType] = ..., *, data=..., **kwargs) -> EventCollection:
...
@_copy_docstring_and_deprecators(Axes.fill)
def fill(*args, data=..., **kwargs) -> list[Polygon]:
...
@_copy_docstring_and_deprecators(Axes.fill_between)
def fill_between(x: ArrayLike, y1: ArrayLike | float, y2: ArrayLike | float = ..., where: Sequence[bool] | None = ..., interpolate: bool = ..., step: Literal["pre", "post", "mid"] | None = ..., *, data=..., **kwargs) -> PolyCollection:
...
@_copy_docstring_and_deprecators(Axes.fill_betweenx)
def fill_betweenx(y: ArrayLike, x1: ArrayLike | float, x2: ArrayLike | float = ..., where: Sequence[bool] | None = ..., step: Literal["pre", "post", "mid"] | None = ..., interpolate: bool = ..., *, data=..., **kwargs) -> PolyCollection:
...
@_copy_docstring_and_deprecators(Axes.grid)
def grid(visible: bool | None = ..., which: Literal["major", "minor", "both"] = ..., axis: Literal["both", "x", "y"] = ..., **kwargs) -> None:
...
@_copy_docstring_and_deprecators(Axes.hexbin)
def hexbin(x: ArrayLike, y: ArrayLike, C: ArrayLike | None = ..., gridsize: int | tuple[int, int] = ..., bins: Literal["log"] | int | Sequence[float] | None = ..., xscale: Literal["linear", "log"] = ..., yscale: Literal["linear", "log"] = ..., extent: tuple[float, float, float, float] | None = ..., cmap: str | Colormap | None = ..., norm: str | Normalize | None = ..., vmin: float | None = ..., vmax: float | None = ..., alpha: float | None = ..., linewidths: float | None = ..., edgecolors: Literal["face", "none"] | ColorType = ..., reduce_C_function: Callable[[np.ndarray | list[float]], float] = ..., mincnt: int | None = ..., marginals: bool = ..., *, data=..., **kwargs) -> PolyCollection:
...
@_copy_docstring_and_deprecators(Axes.hist)
def hist(x: ArrayLike | Sequence[ArrayLike], bins: int | Sequence[float] | str | None = ..., range: tuple[float, float] | None = ..., density: bool = ..., weights: ArrayLike | None = ..., cumulative: bool | float = ..., bottom: ArrayLike | float | None = ..., histtype: Literal["bar", "barstacked", "step", "stepfilled"] = ..., align: Literal["left", "mid", "right"] = ..., orientation: Literal["vertical", "horizontal"] = ..., rwidth: float | None = ..., log: bool = ..., color: ColorType | Sequence[ColorType] | None = ..., label: str | Sequence[str] | None = ..., stacked: bool = ..., *, data=..., **kwargs) -> tuple[np.ndarray | list[np.ndarray], np.ndarray, BarContainer | Polygon | list[BarContainer | Polygon],]:
...
@_copy_docstring_and_deprecators(Axes.stairs)
def stairs(values: ArrayLike, edges: ArrayLike | None = ..., *, orientation: Literal["vertical", "horizontal"] = ..., baseline: float | ArrayLike | None = ..., fill: bool = ..., data=..., **kwargs) -> StepPatch:
...
@_copy_docstring_and_deprecators(Axes.hist2d)
def hist2d(x: ArrayLike, y: ArrayLike, bins: None | int | tuple[int, int] | ArrayLike | tuple[ArrayLike, ArrayLike] = ..., range: ArrayLike | None = ..., density: bool = ..., weights: ArrayLike | None = ..., cmin: float | None = ..., cmax: float | None = ..., *, data=..., **kwargs) -> tuple[np.ndarray, np.ndarray, np.ndarray, QuadMesh]:
...
@_copy_docstring_and_deprecators(Axes.hlines)
def hlines(y: float | ArrayLike, xmin: float | ArrayLike, xmax: float | ArrayLike, colors: ColorType | Sequence[ColorType] | None = ..., linestyles: LineStyleType = ..., label: str = ..., *, data=..., **kwargs) -> LineCollection:
...
@_copy_docstring_and_deprecators(Axes.imshow)
def imshow(X: ArrayLike | PIL.Image.Image, cmap: str | Colormap | None = ..., norm: str | Normalize | None = ..., *, aspect: Literal["equal", "auto"] | float | None = ..., interpolation: str | None = ..., alpha: float | ArrayLike | None = ..., vmin: float | None = ..., vmax: float | None = ..., origin: Literal["upper", "lower"] | None = ..., extent: tuple[float, float, float, float] | None = ..., interpolation_stage: Literal["data", "rgba"] | None = ..., filternorm: bool = ..., filterrad: float = ..., resample: bool | None = ..., url: str | None = ..., data=..., **kwargs) -> AxesImage:
...
@_copy_docstring_and_deprecators(Axes.legend)
def legend(*args, **kwargs) -> Legend:
...
@_copy_docstring_and_deprecators(Axes.locator_params)
def locator_params(axis: Literal["both", "x", "y"] = ..., tight: bool | None = ..., **kwargs) -> None:
...
@_copy_docstring_and_deprecators(Axes.loglog)
def loglog(*args, **kwargs) -> list[Line2D]:
...
@_copy_docstring_and_deprecators(Axes.magnitude_spectrum)
def magnitude_spectrum(x: ArrayLike, Fs: float | None = ..., Fc: int | None = ..., window: Callable[[ArrayLike], ArrayLike] | ArrayLike | None = ..., pad_to: int | None = ..., sides: Literal["default", "onesided", "twosided"] | None = ..., scale: Literal["default", "linear", "dB"] | None = ..., *, data=..., **kwargs) -> tuple[np.ndarray, np.ndarray, Line2D]:
...
@_copy_docstring_and_deprecators(Axes.margins)
def margins(*margins: float, x: float | None = ..., y: float | None = ..., tight: bool | None = ...) -> tuple[float, float] | None:
...
@_copy_docstring_and_deprecators(Axes.minorticks_off)
def minorticks_off() -> None:
...
@_copy_docstring_and_deprecators(Axes.minorticks_on)
def minorticks_on() -> None:
...
@_copy_docstring_and_deprecators(Axes.pcolor)
def pcolor(*args: ArrayLike, shading: Literal["flat", "nearest", "auto"] | None = ..., alpha: float | None = ..., norm: str | Normalize | None = ..., cmap: str | Colormap | None = ..., vmin: float | None = ..., vmax: float | None = ..., data=..., **kwargs) -> Collection:
...
@_copy_docstring_and_deprecators(Axes.pcolormesh)
def pcolormesh(*args: ArrayLike, alpha: float | None = ..., norm: str | Normalize | None = ..., cmap: str | Colormap | None = ..., vmin: float | None = ..., vmax: float | None = ..., shading: Literal["flat", "nearest", "gouraud", "auto"] | None = ..., antialiased: bool = ..., data=..., **kwargs) -> QuadMesh:
...
@_copy_docstring_and_deprecators(Axes.phase_spectrum)
def phase_spectrum(x: ArrayLike, Fs: float | None = ..., Fc: int | None = ..., window: Callable[[ArrayLike], ArrayLike] | ArrayLike | None = ..., pad_to: int | None = ..., sides: Literal["default", "onesided", "twosided"] | None = ..., *, data=..., **kwargs) -> tuple[np.ndarray, np.ndarray, Line2D]:
...
@_copy_docstring_and_deprecators(Axes.pie)
def pie(x: ArrayLike, explode: ArrayLike | None = ..., labels: Sequence[str] | None = ..., colors: ColorType | Sequence[ColorType] | None = ..., autopct: str | Callable[[float], str] | None = ..., pctdistance: float = ..., shadow: bool = ..., labeldistance: float | None = ..., startangle: float = ..., radius: float = ..., counterclock: bool = ..., wedgeprops: dict[str, Any] | None = ..., textprops: dict[str, Any] | None = ..., center: tuple[float, float] = ..., frame: bool = ..., rotatelabels: bool = ..., *, normalize: bool = ..., hatch: str | Sequence[str] | None = ..., data=...) -> tuple[list[Wedge], list[Text]] | tuple[list[Wedge], list[Text], list[Text]]:
...
@_copy_docstring_and_deprecators(Axes.plot)
def plot(*args: float | ArrayLike | str, scalex: bool = ..., scaley: bool = ..., data=..., **kwargs) -> list[Line2D]:
...
@_copy_docstring_and_deprecators(Axes.plot_date)
def plot_date(x: ArrayLike, y: ArrayLike, fmt: str = ..., tz: str | datetime.tzinfo | None = ..., xdate: bool = ..., ydate: bool = ..., *, data=..., **kwargs) -> list[Line2D]:
...
@_copy_docstring_and_deprecators(Axes.psd)
def psd(x: ArrayLike, NFFT: int | None = ..., Fs: float | None = ..., Fc: int | None = ..., detrend: Literal["none", "mean", "linear"] | Callable[[ArrayLike], ArrayLike] | None = ..., window: Callable[[ArrayLike], ArrayLike] | ArrayLike | None = ..., noverlap: int | None = ..., pad_to: int | None = ..., sides: Literal["default", "onesided", "twosided"] | None = ..., scale_by_freq: bool | None = ..., return_line: bool | None = ..., *, data=..., **kwargs) -> tuple[np.ndarray, np.ndarray] | tuple[np.ndarray, np.ndarray, Line2D]:
...
@_copy_docstring_and_deprecators(Axes.quiver)
def quiver(*args, data=..., **kwargs) -> Quiver:
...
@_copy_docstring_and_deprecators(Axes.quiverkey)
def quiverkey(Q: Quiver, X: float, Y: float, U: float, label: str, **kwargs) -> QuiverKey:
...
@_copy_docstring_and_deprecators(Axes.scatter)
def scatter(x: float | ArrayLike, y: float | ArrayLike, s: float | ArrayLike | None = ..., c: Sequence[ColorType] | ColorType | None = ..., marker: MarkerType | None = ..., cmap: str | Colormap | None = ..., norm: str | Normalize | None = ..., vmin: float | None = ..., vmax: float | None = ..., alpha: float | None = ..., linewidths: float | Sequence[float] | None = ..., *, edgecolors: Literal["face", "none"] | ColorType | Sequence[ColorType] | None = ..., plotnonfinite: bool = ..., data=..., **kwargs) -> PathCollection:
...
@_copy_docstring_and_deprecators(Axes.semilogx)
def semilogx(*args, **kwargs) -> list[Line2D]:
...
@_copy_docstring_and_deprecators(Axes.semilogy)
def semilogy(*args, **kwargs) -> list[Line2D]:
...
@_copy_docstring_and_deprecators(Axes.specgram)
def specgram(x: ArrayLike, NFFT: int | None = ..., Fs: float | None = ..., Fc: int | None = ..., detrend: Literal["none", "mean", "linear"] | Callable[[ArrayLike], ArrayLike] | None = ..., window: Callable[[ArrayLike], ArrayLike] | ArrayLike | None = ..., noverlap: int | None = ..., cmap: str | Colormap | None = ..., xextent: tuple[float, float] | None = ..., pad_to: int | None = ..., sides: Literal["default", "onesided", "twosided"] | None = ..., scale_by_freq: bool | None = ..., mode: Literal["default", "psd", "magnitude", "angle", "phase"] | None = ..., scale: Literal["default", "linear", "dB"] | None = ..., vmin: float | None = ..., vmax: float | None = ..., *, data=..., **kwargs) -> tuple[np.ndarray, np.ndarray, np.ndarray, AxesImage]:
...
@_copy_docstring_and_deprecators(Axes.spy)
def spy(Z: ArrayLike, precision: float | Literal["present"] = ..., marker: str | None = ..., markersize: float | None = ..., aspect: Literal["equal", "auto"] | float | None = ..., origin: Literal["upper", "lower"] = ..., **kwargs) -> AxesImage:
...
@_copy_docstring_and_deprecators(Axes.stackplot)
def stackplot(x, *args, labels=..., colors=..., baseline=..., data=..., **kwargs): # -> list[PolyCollection]:
...
@_copy_docstring_and_deprecators(Axes.stem)
def stem(*args: ArrayLike | str, linefmt: str | None = ..., markerfmt: str | None = ..., basefmt: str | None = ..., bottom: float = ..., label: str | None = ..., orientation: Literal["vertical", "horizontal"] = ..., data=...) -> StemContainer:
...
@_copy_docstring_and_deprecators(Axes.step)
def step(x: ArrayLike, y: ArrayLike, *args, where: Literal["pre", "post", "mid"] = ..., data=..., **kwargs) -> list[Line2D]:
...
@_copy_docstring_and_deprecators(Axes.streamplot)
def streamplot(x, y, u, v, density=..., linewidth=..., color=..., cmap=..., norm=..., arrowsize=..., arrowstyle=..., minlength=..., transform=..., zorder=..., start_points=..., maxlength=..., integration_direction=..., broken_streamlines=..., *, data=...): # -> StreamplotSet:
...
@_copy_docstring_and_deprecators(Axes.table)
def table(cellText=..., cellColours=..., cellLoc=..., colWidths=..., rowLabels=..., rowColours=..., rowLoc=..., colLabels=..., colColours=..., colLoc=..., loc=..., bbox=..., edges=..., **kwargs): # -> Table:
...
@_copy_docstring_and_deprecators(Axes.text)
def text(x: float, y: float, s: str, fontdict: dict[str, Any] | None = ..., **kwargs) -> Text:
...
@_copy_docstring_and_deprecators(Axes.tick_params)
def tick_params(axis: Literal["both", "x", "y"] = ..., **kwargs) -> None:
...
@_copy_docstring_and_deprecators(Axes.ticklabel_format)
def ticklabel_format(*, axis: Literal["both", "x", "y"] = ..., style: Literal["", "sci", "scientific", "plain"] = ..., scilimits: tuple[int, int] | None = ..., useOffset: bool | float | None = ..., useLocale: bool | None = ..., useMathText: bool | None = ...) -> None:
...
@_copy_docstring_and_deprecators(Axes.tricontour)
def tricontour(*args, **kwargs): # -> TriContourSet:
...
@_copy_docstring_and_deprecators(Axes.tricontourf)
def tricontourf(*args, **kwargs): # -> TriContourSet:
...
@_copy_docstring_and_deprecators(Axes.tripcolor)
def tripcolor(*args, alpha=..., norm=..., cmap=..., vmin=..., vmax=..., shading=..., facecolors=..., **kwargs):
...
@_copy_docstring_and_deprecators(Axes.triplot)
def triplot(*args, **kwargs): # -> tuple[Line2D, Line2D]:
...
@_copy_docstring_and_deprecators(Axes.violinplot)
def violinplot(dataset: ArrayLike | Sequence[ArrayLike], positions: ArrayLike | None = ..., vert: bool = ..., widths: float | ArrayLike = ..., showmeans: bool = ..., showextrema: bool = ..., showmedians: bool = ..., quantiles: Sequence[float | Sequence[float]] | None = ..., points: int = ..., bw_method: Literal["scott", "silverman"] | float | Callable[[GaussianKDE], float] | None = ..., *, data=...) -> dict[str, Collection]:
...
@_copy_docstring_and_deprecators(Axes.vlines)
def vlines(x: float | ArrayLike, ymin: float | ArrayLike, ymax: float | ArrayLike, colors: ColorType | Sequence[ColorType] | None = ..., linestyles: LineStyleType = ..., label: str = ..., *, data=..., **kwargs) -> LineCollection:
...
@_copy_docstring_and_deprecators(Axes.xcorr)
def xcorr(x: ArrayLike, y: ArrayLike, normed: bool = ..., detrend: Callable[[ArrayLike], ArrayLike] = ..., usevlines: bool = ..., maxlags: int = ..., *, data=..., **kwargs) -> tuple[np.ndarray, np.ndarray, LineCollection | Line2D, Line2D | None]:
...
@_copy_docstring_and_deprecators(Axes._sci)
def sci(im: ScalarMappable) -> None:
...
@_copy_docstring_and_deprecators(Axes.set_title)
def title(label: str, fontdict: dict[str, Any] | None = ..., loc: Literal["left", "center", "right"] | None = ..., pad: float | None = ..., *, y: float | None = ..., **kwargs) -> Text:
...
@_copy_docstring_and_deprecators(Axes.set_xlabel)
def xlabel(xlabel: str, fontdict: dict[str, Any] | None = ..., labelpad: float | None = ..., *, loc: Literal["left", "center", "right"] | None = ..., **kwargs) -> Text:
...
@_copy_docstring_and_deprecators(Axes.set_ylabel)
def ylabel(ylabel: str, fontdict: dict[str, Any] | None = ..., labelpad: float | None = ..., *, loc: Literal["bottom", "center", "top"] | None = ..., **kwargs) -> Text:
...
@_copy_docstring_and_deprecators(Axes.set_xscale)
def xscale(value: str | ScaleBase, **kwargs) -> None:
...
@_copy_docstring_and_deprecators(Axes.set_yscale)
def yscale(value: str | ScaleBase, **kwargs) -> None:
...
def autumn() -> None:
"""
Set the colormap to 'autumn'.
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
...
def bone() -> None:
"""
Set the colormap to 'bone'.
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
...
def cool() -> None:
"""
Set the colormap to 'cool'.
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
...
def copper() -> None:
"""
Set the colormap to 'copper'.
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
...
def flag() -> None:
"""
Set the colormap to 'flag'.
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
...
def gray() -> None:
"""
Set the colormap to 'gray'.
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
...
def hot() -> None:
"""
Set the colormap to 'hot'.
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
...
def hsv() -> None:
"""
Set the colormap to 'hsv'.
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
...
def jet() -> None:
"""
Set the colormap to 'jet'.
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
...
def pink() -> None:
"""
Set the colormap to 'pink'.
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
...
def prism() -> None:
"""
Set the colormap to 'prism'.
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
...
def spring() -> None:
"""
Set the colormap to 'spring'.
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
...
def summer() -> None:
"""
Set the colormap to 'summer'.
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
...
def winter() -> None:
"""
Set the colormap to 'winter'.
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
...
def magma() -> None:
"""
Set the colormap to 'magma'.
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
...
def inferno() -> None:
"""
Set the colormap to 'inferno'.
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
...
def plasma() -> None:
"""
Set the colormap to 'plasma'.
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
...
def viridis() -> None:
"""
Set the colormap to 'viridis'.
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
...
def nipy_spectral() -> None:
"""
Set the colormap to 'nipy_spectral'.
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
...