SpringFocus/venv/lib/python3.8/site-packages/matplotlib/pyplot.py

# 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)


@docstring.copy(matplotlib.rc_context)
def rc_context(rc=None, fname=None):
    return matplotlib.rc_context(rc, fname)


@docstring.copy(matplotlib.rcdefaults)
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

        _pylab_helpers.Gcf.set_active(figManager)
        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:
        figManager.canvas.figure.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()
            and not fig.canvas._is_idle_drawing):
        # Some artists can mark themselves as stale in the middle of drawing
        # (e.g. axes position & tick labels being computed at draw time), but
        # this shouldn't trigger a redraw because the current redraw will
        # already take them into account.
        with fig.canvas._idle_draw_cntx():
            fig.canvas.draw_idle()


def gcf():
    """
    Get the current figure.

    If no current figure exists, a new one is created using
    `~.pyplot.figure()`.
    """
    figManager = _pylab_helpers.Gcf.get_active()
    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)


@docstring.copy(FigureCanvasBase.mpl_disconnect)
def disconnect(cid):
    return gcf().canvas.mpl_disconnect(cid)


def close(fig=None):
    """
    Close a figure window.

    Parameters
    ----------
    fig : None or int or str or `.Figure`
        The figure to close. There are a number of ways to specify this:

        - *None*: the current figure
        - `.Figure`: the given `.Figure` instance
        - ``int``: a figure number
        - ``str``: a figure name
        - 'all': all figures

    """
    if fig is None:
        figManager = _pylab_helpers.Gcf.get_active()
        if figManager is None:
            return
        else:
            _pylab_helpers.Gcf.destroy(figManager.num)
    elif fig == 'all':
        _pylab_helpers.Gcf.destroy_all()
    elif isinstance(fig, int):
        _pylab_helpers.Gcf.destroy(fig)
    elif hasattr(fig, 'int'):
        # if we are dealing with a type UUID, we
        # can use its integer representation
        _pylab_helpers.Gcf.destroy(fig.int)
    elif isinstance(fig, str):
        allLabels = get_figlabels()
        if fig in allLabels:
            num = get_fignums()[allLabels.index(fig)]
            _pylab_helpers.Gcf.destroy(num)
    elif isinstance(fig, Figure):
        _pylab_helpers.Gcf.destroy_fig(fig)
    else:
        raise TypeError("close() argument must be a Figure, an int, a string, "
                        "or None, not '%s'")


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:
            _pylab_helpers.Gcf.set_active(m)
            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)

    Parameters
    ----------
    *args
        Either a 3-digit integer or three separate integers
        describing the position of the subplot. If the three
        integers are *nrows*, *ncols*, and *index* in order, 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.

        *pos* is a three digit integer, where the first digit is the
        number of rows, the second the number of columns, and the third
        the index of the subplot. i.e. fig.add_subplot(235) is the same as
        fig.add_subplot(2, 3, 5). Note that all integers must be less than
        10 for this form to work.

    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 : 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
        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)s

    Returns
    -------
    axes : an `.axes.SubplotBase` subclass of `~.axes.Axes` (or a subclass \
    of `~.axes.Axes`)

        The axes of the subplot. The returned axes base class depends on
        the projection used. It is `~.axes.Axes` if rectilinear projection
        are used and `.projections.polar.PolarAxes` if polar projection
        are used. The returned axes is then a subplot subclass of the
        base class.

    Notes
    -----
    Creating a subplot will delete any pre-existing subplot 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 the figure already has a subplot with key (*args*,
    *kwargs*) then it will simply make that subplot 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 subplot), you must use a unique set of args and kwargs.  The axes
    *label* attribute has been exposed for this purpose: if you want
    two subplots that are otherwise identical to be added to the figure,
    make sure you give them unique labels.

    In rare circumstances, `.add_subplot` may be called with a single
    argument, a subplot axes instance already created in the
    present figure but not in the figure's list of axes.

    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)
    """

    # if subplot called without arguments, create subplot(1, 1, 1)
    if len(args) == 0:
        args = (1, 1, 1)

    # This check was added because it is very easy to type
    # subplot(1, 2, False) when subplots(1, 2, False) was intended
    # (sharex=False, that is). In most cases, no error will
    # ever occur, but mysterious behavior can result because what was
    # intended to be the sharex argument is instead treated as a
    # subplot index for subplot()
    if len(args) >= 3 and isinstance(args[2], bool):
        cbook._warn_external("The subplot index argument to subplot() appears "
                             "to be a boolean. Did you intend to use "
                             "subplots()?")

    fig = gcf()
    a = fig.add_subplot(*args, **kwargs)
    bbox = a.bbox
    byebye = []
    for other in fig.axes:
        if other == a:
            continue
        if bbox.fully_overlaps(other.bbox):
            byebye.append(other)
    for ax in byebye:
        delaxes(ax)

    return a


def subplots(nrows=1, ncols=1, sharex=False, sharey=False, squeeze=True,
             subplot_kw=None, gridspec_kw=None, **fig_kw):
    """
    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, optional, 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`.

    squeeze : bool, optional, 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.

    num : int or str, optional, default: None
        A `.pyplot.figure` keyword that sets the figure number or label.

    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.Figure`

    ax : `.axes.Axes` object or array of Axes objects.
        *ax* can be either a single `~matplotlib.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.subplot(1, 2)
            fig, ((ax1, ax2), (ax3, ax4)) = plt.subplot(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.

    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(polar=True))
        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)

    See Also
    --------
    .pyplot.figure
    .pyplot.subplot
    .pyplot.axes
    .Figure.subplots
    .Figure.add_subplot

    """
    fig = figure(**fig_kw)
    axs = fig.subplots(nrows=nrows, ncols=ncols, sharex=sharex, sharey=sharey,
                       squeeze=squeeze, subplot_kw=subplot_kw,
                       gridspec_kw=gridspec_kw)
    return fig, axs


def subplot2grid(shape, loc, rowspan=1, colspan=1, fig=None, **kwargs):
    """
    Create an axis at specific location inside a regular grid.

    Parameters
    ----------
    shape : sequence of 2 ints
        Shape of grid in which to place axis.
        First entry is number of rows, second entry is number of columns.

    loc : sequence of 2 ints
        Location to place axis within grid.
        First entry is row number, second entry is column number.

    rowspan : int
        Number of rows for the axis to span to the right.

    colspan : int
        Number of columns for the axis to span downwards.

    fig : `Figure`, optional
        Figure to place axis in. Defaults to current figure.

    **kwargs
        Additional keyword arguments are handed to `add_subplot`.


    Notes
    -----
    The following call ::

        subplot2grid(shape, loc, rowspan=1, colspan=1)

    is identical to ::

        gridspec=GridSpec(shape[0], shape[1])
        subplotspec=gridspec.new_subplotspec(loc, rowspan, colspan)
        subplot(subplotspec)
    """

    if fig is None:
        fig = gcf()

    s1, s2 = shape
    subplotspec = GridSpec(s1, s2).new_subplotspec(loc,
                                                   rowspan=rowspan,
                                                   colspan=colspan)
    a = fig.add_subplot(subplotspec, **kwargs)
    bbox = a.bbox
    byebye = []
    for other in fig.axes:
        if other == a:
            continue
        if bbox.fully_overlaps(other.bbox):
            byebye.append(other)
    for ax in byebye:
        delaxes(ax)

    return a


def twinx(ax=None):
    """
    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`
    """
    if ax is None:
        ax = gca()
    ax1 = ax.twinx()
    return ax1


def twiny(ax=None):
    """
    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`
    """
    if ax is None:
        ax = gca()
    ax1 = ax.twiny()
    return ax1


def subplots_adjust(left=None, bottom=None, right=None, top=None,
                    wspace=None, hspace=None):
    """
    Tune the subplot layout.

    The parameter meanings (and suggested defaults) are::

      left = 0.125  # the left side of the subplots of the figure
      right = 0.9   # the right side of the subplots of the figure
      bottom = 0.1  # the bottom of the subplots of the figure
      top = 0.9     # the top of the subplots of the figure
      wspace = 0.2  # the amount of width reserved for space between subplots,
                    # expressed as a fraction of the average axis width
      hspace = 0.2  # the amount of height reserved for space between subplots,
                    # expressed as a fraction of the average axis height

    The actual defaults are controlled by the rc file
    """
    fig = gcf()
    fig.subplots_adjust(left, bottom, right, top, wspace, hspace)


def subplot_tool(targetfig=None):
    """
    Launch a subplot tool window for a figure.

    A :class:`matplotlib.widgets.SubplotTool` instance is returned.
    """
    if targetfig is None:
        targetfig = gcf()

    tbar = rcParams['toolbar']  # Turn off navigation toolbar for the toolfig.
    rcParams['toolbar'] = 'None'
    toolfig = figure(figsize=(6, 3))
    toolfig.subplots_adjust(top=0.9)
    rcParams['toolbar'] = tbar

    if hasattr(targetfig.canvas, "manager"):  # Restore the current figure.
        _pylab_helpers.Gcf.set_active(targetfig.canvas.manager)

    return SubplotTool(targetfig, toolfig)


def tight_layout(pad=1.08, h_pad=None, w_pad=None, rect=None):
    """
    Automatically adjust subplot parameters to give specified padding.

    Parameters
    ----------
    pad : float
        Padding between the figure edge and the edges of subplots,
        as a fraction of the font size.
    h_pad, w_pad : float, optional
        Padding (height/width) between edges of adjacent subplots,
        as a fraction of the font size.  Defaults to *pad*.
    rect : tuple (left, bottom, right, top), optional
        A rectangle (left, bottom, right, top) in the normalized
        figure coordinate that the whole subplots area (including
        labels) will fit into. Default is (0, 0, 1, 1).
    """
    gcf().tight_layout(pad=pad, h_pad=h_pad, w_pad=w_pad, rect=rect)


def box(on=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`
    """
    ax = gca()
    if on is None:
        on = not ax.get_frame_on()
    ax.set_frame_on(on)

## Axis ##


def xlim(*args, **kwargs):
    """
    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.
    """
    ax = gca()
    if not args and not kwargs:
        return ax.get_xlim()
    ret = ax.set_xlim(*args, **kwargs)
    return ret


def ylim(*args, **kwargs):
    """
    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.
    """
    ax = gca()
    if not args and not kwargs:
        return ax.get_ylim()
    ret = ax.set_ylim(*args, **kwargs)
    return ret


def xticks(ticks=None, labels=None, **kwargs):
    """
    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.
    **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.
    """
    ax = gca()

    if ticks is None and labels is None:
        locs = ax.get_xticks()
        labels = ax.get_xticklabels()
    elif labels is None:
        locs = ax.set_xticks(ticks)
        labels = ax.get_xticklabels()
    else:
        locs = ax.set_xticks(ticks)
        labels = ax.set_xticklabels(labels, **kwargs)
    for l in labels:
        l.update(kwargs)

    return locs, labels


def yticks(ticks=None, labels=None, **kwargs):
    """
    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 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.
    **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.
    """
    ax = gca()

    if ticks is None and labels is None:
        locs = ax.get_yticks()
        labels = ax.get_yticklabels()
    elif labels is None:
        locs = ax.set_yticks(ticks)
        labels = ax.get_yticklabels()
    else:
        locs = ax.set_yticks(ticks)
        labels = ax.set_yticklabels(labels, **kwargs)
    for l in labels:
        l.update(kwargs)

    return locs, labels


def rgrids(*args, **kwargs):
    """
    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, labels : list of `.lines.Line2D`, list of `.text.Text`
        *lines* are the radial gridlines and *labels* are the tick labels.

    Other Parameters
    ----------------
    **kwargs
        *kwargs* are optional `~.Text` properties for the labels.

    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' ))

    See Also
    --------
    .pyplot.thetagrids
    .projections.polar.PolarAxes.set_rgrids
    .Axis.get_gridlines
    .Axis.get_ticklabels
    """
    ax = gca()
    if not isinstance(ax, PolarAxes):
        raise RuntimeError('rgrids only defined for polar axes')
    if len(args) == 0:
        lines = ax.yaxis.get_gridlines()
        labels = ax.yaxis.get_ticklabels()
    else:
        lines, labels = ax.set_rgrids(*args, **kwargs)
    return lines, labels


def thetagrids(*args, **kwargs):
    """
    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, labels : list of `.lines.Line2D`, list of `.text.Text`
        *lines* are the theta gridlines and *labels* are the tick labels.

    Other Parameters
    ----------------
    **kwargs
        *kwargs* are optional `~.Text` properties for the labels.

    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'))

    See Also
    --------
    .pyplot.rgrids
    .projections.polar.PolarAxes.set_thetagrids
    .Axis.get_gridlines
    .Axis.get_ticklabels
    """
    ax = gca()
    if not isinstance(ax, PolarAxes):
        raise RuntimeError('thetagrids only defined for polar axes')
    if len(args) == 0:
        lines = ax.xaxis.get_ticklines()
        labels = ax.xaxis.get_ticklabels()
    else:
        lines, labels = ax.set_thetagrids(*args, **kwargs)
    return lines, labels


## Plotting Info ##


def plotting():
    pass


def get_plot_commands():
    """
    Get a sorted list of all of the plotting commands.
    """
    # This works by searching for all functions in this module and removing
    # a few hard-coded exclusions, as well as all of the colormap-setting
    # functions, and anything marked as private with a preceding underscore.
    exclude = {'colormaps', 'colors', 'connect', 'disconnect',
               'get_plot_commands', 'get_current_fig_manager', 'ginput',
               'plotting', 'waitforbuttonpress'}
    exclude |= set(colormaps())
    this_module = inspect.getmodule(get_plot_commands)
    return sorted(
        name for name, obj in globals().items()
        if not name.startswith('_') and name not in exclude
           and inspect.isfunction(obj)
           and inspect.getmodule(obj) is this_module)


def colormaps():
    """
    Matplotlib provides a number of colormaps, and others can be added using
    :func:`~matplotlib.cm.register_cmap`.  This function documents the built-in
    colormaps, and will also return a list of all registered colormaps if
    called.

    You can set the colormap for an image, pcolor, scatter, etc,
    using a keyword argument::

      imshow(X, cmap=cm.hot)

    or using the :func:`set_cmap` function::

      imshow(X)
      pyplot.set_cmap('hot')
      pyplot.set_cmap('jet')

    In interactive mode, :func:`set_cmap` will update the colormap post-hoc,
    allowing you to see which one works best for your data.

    All built-in colormaps can be reversed by appending ``_r``: For instance,
    ``gray_r`` is the reverse of ``gray``.

    There are several common color schemes used in visualization:

    Sequential schemes
      for unipolar data that progresses from low to high
    Diverging schemes
      for bipolar data that emphasizes positive or negative deviations from a
      central value
    Cyclic schemes
      for plotting values that wrap around at the endpoints, such as phase
      angle, wind direction, or time of day
    Qualitative schemes
      for nominal data that has no inherent ordering, where color is used
      only to distinguish categories

    Matplotlib ships with 4 perceptually uniform color maps which are
    the recommended color maps for sequential data:

      =========   ===================================================
      Colormap    Description
      =========   ===================================================
      inferno     perceptually uniform shades of black-red-yellow
      magma       perceptually uniform shades of black-red-white
      plasma      perceptually uniform shades of blue-red-yellow
      viridis     perceptually uniform shades of blue-green-yellow
      =========   ===================================================

    The following colormaps are based on the `ColorBrewer
    <http://colorbrewer2.org>`_ color specifications and designs developed by
    Cynthia Brewer:

    ColorBrewer Diverging (luminance is highest at the midpoint, and
    decreases towards differently-colored endpoints):

      ========  ===================================
      Colormap  Description
      ========  ===================================
      BrBG      brown, white, blue-green
      PiYG      pink, white, yellow-green
      PRGn      purple, white, green
      PuOr      orange, white, purple
      RdBu      red, white, blue
      RdGy      red, white, gray
      RdYlBu    red, yellow, blue
      RdYlGn    red, yellow, green
      Spectral  red, orange, yellow, green, blue
      ========  ===================================

    ColorBrewer Sequential (luminance decreases monotonically):

      ========  ====================================
      Colormap  Description
      ========  ====================================
      Blues     white to dark blue
      BuGn      white, light blue, dark green
      BuPu      white, light blue, dark purple
      GnBu      white, light green, dark blue
      Greens    white to dark green
      Greys     white to black (not linear)
      Oranges   white, orange, dark brown
      OrRd      white, orange, dark red
      PuBu      white, light purple, dark blue
      PuBuGn    white, light purple, dark green
      PuRd      white, light purple, dark red
      Purples   white to dark purple
      RdPu      white, pink, dark purple
      Reds      white to dark red
      YlGn      light yellow, dark green
      YlGnBu    light yellow, light green, dark blue
      YlOrBr    light yellow, orange, dark brown
      YlOrRd    light yellow, orange, dark red
      ========  ====================================

    ColorBrewer Qualitative:

    (For plotting nominal data, :class:`ListedColormap` is used,
    not :class:`LinearSegmentedColormap`.  Different sets of colors are
    recommended for different numbers of categories.)

    * Accent
    * Dark2
    * Paired
    * Pastel1
    * Pastel2
    * Set1
    * Set2
    * Set3

    A set of colormaps derived from those of the same name provided
    with Matlab are also included:

      =========   =======================================================
      Colormap    Description
      =========   =======================================================
      autumn      sequential linearly-increasing shades of red-orange-yellow
      bone        sequential increasing black-white color map with
                  a tinge of blue, to emulate X-ray film
      cool        linearly-decreasing shades of cyan-magenta
      copper      sequential increasing shades of black-copper
      flag        repetitive red-white-blue-black pattern (not cyclic at
                  endpoints)
      gray        sequential linearly-increasing black-to-white
                  grayscale
      hot         sequential black-red-yellow-white, to emulate blackbody
                  radiation from an object at increasing temperatures
      jet         a spectral map with dark endpoints, blue-cyan-yellow-red;
                  based on a fluid-jet simulation by NCSA [#]_
      pink        sequential increasing pastel black-pink-white, meant
                  for sepia tone colorization of photographs
      prism       repetitive red-yellow-green-blue-purple-...-green pattern
                  (not cyclic at endpoints)
      spring      linearly-increasing shades of magenta-yellow
      summer      sequential linearly-increasing shades of green-yellow
      winter      linearly-increasing shades of blue-green
      =========   =======================================================

    A set of palettes from the `Yorick scientific visualisation
    package <https://dhmunro.github.io/yorick-doc/>`_, an evolution of
    the GIST package, both by David H. Munro are included:

      ============  =======================================================
      Colormap      Description
      ============  =======================================================
      gist_earth    mapmaker's colors from dark blue deep ocean to green
                    lowlands to brown highlands to white mountains
      gist_heat     sequential increasing black-red-orange-white, to emulate
                    blackbody radiation from an iron bar as it grows hotter
      gist_ncar     pseudo-spectral black-blue-green-yellow-red-purple-white
                    colormap from National Center for Atmospheric
                    Research [#]_
      gist_rainbow  runs through the colors in spectral order from red to
                    violet at full saturation (like *hsv* but not cyclic)
      gist_stern    "Stern special" color table from Interactive Data
                    Language software
      ============  =======================================================

    A set of cyclic color maps:

      ================  =================================================
      Colormap          Description
      ================  =================================================
      hsv               red-yellow-green-cyan-blue-magenta-red, formed by
                        changing the hue component in the HSV color space
      twilight          perceptually uniform shades of
                        white-blue-black-red-white
      twilight_shifted  perceptually uniform shades of
                        black-blue-white-red-black
      ================  =================================================


    Other miscellaneous schemes:

      ============= =======================================================
      Colormap      Description
      ============= =======================================================
      afmhot        sequential black-orange-yellow-white blackbody
                    spectrum, commonly used in atomic force microscopy
      brg           blue-red-green
      bwr           diverging blue-white-red
      coolwarm      diverging blue-gray-red, meant to avoid issues with 3D
                    shading, color blindness, and ordering of colors [#]_
      CMRmap        "Default colormaps on color images often reproduce to
                    confusing grayscale images. The proposed colormap
                    maintains an aesthetically pleasing color image that
                    automatically reproduces to a monotonic grayscale with
                    discrete, quantifiable saturation levels." [#]_
      cubehelix     Unlike most other color schemes cubehelix was designed
                    by D.A. Green to be monotonically increasing in terms
                    of perceived brightness. Also, when printed on a black
                    and white postscript printer, the scheme results in a
                    greyscale with monotonically increasing brightness.
                    This color scheme is named cubehelix because the (r, g, b)
                    values produced can be visualised as a squashed helix
                    around the diagonal in the (r, g, b) color cube.
      gnuplot       gnuplot's traditional pm3d scheme
                    (black-blue-red-yellow)
      gnuplot2      sequential color printable as gray
                    (black-blue-violet-yellow-white)
      ocean         green-blue-white
      rainbow       spectral purple-blue-green-yellow-orange-red colormap
                    with diverging luminance
      seismic       diverging blue-white-red
      nipy_spectral black-purple-blue-green-yellow-red-white spectrum,
                    originally from the Neuroimaging in Python project
      terrain       mapmaker's colors, blue-green-yellow-brown-white,
                    originally from IGOR Pro
      ============= =======================================================

    The following colormaps are redundant and may be removed in future
    versions.  It's recommended to use the names in the descriptions
    instead, which produce identical output:

      =========  =======================================================
      Colormap   Description
      =========  =======================================================
      gist_gray  identical to *gray*
      gist_yarg  identical to *gray_r*
      binary     identical to *gray_r*
      =========  =======================================================

    .. rubric:: Footnotes

    .. [#] Rainbow colormaps, ``jet`` in particular, are considered a poor
      choice for scientific visualization by many researchers: `Rainbow Color
      Map (Still) Considered Harmful
      <http://ieeexplore.ieee.org/document/4118486/?arnumber=4118486>`_

    .. [#] Resembles "BkBlAqGrYeOrReViWh200" from NCAR Command
      Language. See `Color Table Gallery
      <https://www.ncl.ucar.edu/Document/Graphics/color_table_gallery.shtml>`_

    .. [#] See `Diverging Color Maps for Scientific Visualization
      <http://www.kennethmoreland.com/color-maps/>`_ by Kenneth Moreland.

    .. [#] See `A Color Map for Effective Black-and-White Rendering of
      Color-Scale Images
      <https://www.mathworks.com/matlabcentral/fileexchange/2662-cmrmap-m>`_
      by Carey Rappaport
    """
    return sorted(cm.cmap_d)


def _setup_pyplot_info_docstrings():
    """
    Generates the plotting docstring.

    These must be done after the entire module is imported, so it is
    called from the end of this module, which is generated by
    boilerplate.py.
    """
    commands = get_plot_commands()

    first_sentence = re.compile(r"(?:\s*).+?\.(?:\s+|$)", flags=re.DOTALL)

    # Collect the first sentence of the docstring for all of the
    # plotting commands.
    rows = []
    max_name = len("Function")
    max_summary = len("Description")
    for name in commands:
        doc = globals()[name].__doc__
        summary = ''
        if doc is not None:
            match = first_sentence.match(doc)
            if match is not None:
                summary = inspect.cleandoc(match.group(0)).replace('\n', ' ')
        name = '`%s`' % name
        rows.append([name, summary])
        max_name = max(max_name, len(name))
        max_summary = max(max_summary, len(summary))

    separator = '=' * max_name + ' ' + '=' * max_summary
    lines = [
        separator,
        '{:{}} {:{}}'.format('Function', max_name, 'Description', max_summary),
        separator,
    ] + [
        '{:{}} {:{}}'.format(name, max_name, summary, max_summary)
        for name, summary in rows
    ] + [
        separator,
    ]
    plotting.__doc__ = '\n'.join(lines)


## Plotting part 1: manually generated functions and wrappers ##


def colorbar(mappable=None, cax=None, ax=None, **kw):
    if mappable is None:
        mappable = gci()
        if mappable is None:
            raise RuntimeError('No mappable was found to use for colorbar '
                               'creation. First define a mappable such as '
                               'an image (with imshow) or a contour set ('
                               'with contourf).')
    if ax is None:
        ax = gca()
    ret = gcf().colorbar(mappable, cax=cax, ax=ax, **kw)
    return ret
colorbar.__doc__ = matplotlib.colorbar.colorbar_doc


def clim(vmin=None, vmax=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)

    """
    im = gci()
    if im is None:
        raise RuntimeError('You must first define an image, e.g., with imshow')

    im.set_clim(vmin, vmax)


def set_cmap(cmap):
    """
    Set the default colormap, and applies it to the current image if any.

    Parameters
    ----------
    cmap : `~matplotib.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
    """
    cmap = cm.get_cmap(cmap)

    rc('image', cmap=cmap.name)
    im = gci()

    if im is not None:
        im.set_cmap(cmap)


@docstring.copy(matplotlib.image.imread)
def imread(fname, format=None):
    return matplotlib.image.imread(fname, format)


@docstring.copy(matplotlib.image.imsave)
def imsave(fname, arr, **kwargs):
    return matplotlib.image.imsave(fname, arr, **kwargs)


def matshow(A, fignum=None, **kwargs):
    """
    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 : array-like(M, N)
        The matrix to be displayed.

    fignum : None or int or False
        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
    -------
    image : `~matplotlib.image.AxesImage`

    Other Parameters
    ----------------
    **kwargs : `~matplotlib.axes.Axes.imshow` arguments

    """
    A = np.asanyarray(A)
    if fignum == 0:
        ax = gca()
    else:
        # Extract actual aspect ratio of array and make appropriately sized
        # figure.
        fig = figure(fignum, figsize=figaspect(A))
        ax = fig.add_axes([0.15, 0.09, 0.775, 0.775])
    im = ax.matshow(A, **kwargs)
    sci(im)
    return im


def polar(*args, **kwargs):
    """
    Make a polar plot.

    call signature::

      polar(theta, r, **kwargs)

    Multiple *theta*, *r* arguments are supported, with format strings, as in
    `plot`.
    """
    # If an axis already exists, check if it has a polar projection
    if gcf().get_axes():
        if not isinstance(gca(), PolarAxes):
            cbook._warn_external('Trying to create polar plot on an axis '
                                 'that does not have a polar projection.')
    ax = gca(polar=True)
    ret = ax.plot(*args, **kwargs)
    return ret


@cbook.deprecated("3.1")
def plotfile(fname, cols=(0,), plotfuncs=None,
             comments='#', skiprows=0, checkrows=5, delimiter=',',
             names=None, subplots=True, newfig=True, **kwargs):
    """
    Plot the data in a file.

    *cols* is a sequence of column identifiers to plot.  An identifier
    is either an int or a string.  If it is an int, it indicates the
    column number.  If it is a string, it indicates the column header.
    matplotlib will make column headers lower case, replace spaces with
    underscores, and remove all illegal characters; so ``'Adj Close*'``
    will have name ``'adj_close'``.

    - If len(*cols*) == 1, only that column will be plotted on the *y* axis.

    - If len(*cols*) > 1, the first element will be an identifier for
      data for the *x* axis and the remaining elements will be the
      column indexes for multiple subplots if *subplots* is *True*
      (the default), or for lines in a single subplot if *subplots*
      is *False*.

    *plotfuncs*, if not *None*, is a dictionary mapping identifier to
    an :class:`~matplotlib.axes.Axes` plotting function as a string.
    Default is 'plot', other choices are 'semilogy', 'fill', 'bar',
    etc.  You must use the same type of identifier in the *cols*
    vector as you use in the *plotfuncs* dictionary, e.g., integer
    column numbers in both or column names in both. If *subplots*
    is *False*, then including any function such as 'semilogy'
    that changes the axis scaling will set the scaling for all
    columns.

    - *comments*: the character used to indicate the start of a comment
      in the file, or *None* to switch off the removal of comments

    - *skiprows*: is the number of rows from the top to skip

    - *checkrows*: is the number of rows to check to validate the column
      data type.  When set to zero all rows are validated.

    - *delimiter*: is the character(s) separating row items

    - *names*: if not None, is a list of header names.  In this case, no
      header will be read from the file

    If *newfig* is *True*, the plot always will be made in a new figure;
    if *False*, it will be made in the current figure if one exists,
    else in a new figure.

    kwargs are passed on to plotting functions.

    Example usage::

      # plot the 2nd and 4th column against the 1st in two subplots
      plotfile(fname, (0, 1, 3))

      # plot using column names; specify an alternate plot type for volume
      plotfile(fname, ('date', 'volume', 'adj_close'),
               plotfuncs={'volume': 'semilogy'})

    Note: plotfile is intended as a convenience for quickly plotting
    data from flat files; it is not intended as an alternative
    interface to general plotting with pyplot or matplotlib.
    """

    if newfig:
        fig = figure()
    else:
        fig = gcf()

    if len(cols) < 1:
        raise ValueError('must have at least one column of data')

    if plotfuncs is None:
        plotfuncs = {}
    with cbook._suppress_matplotlib_deprecation_warning():
        r = mlab._csv2rec(fname, comments=comments, skiprows=skiprows,
                          checkrows=checkrows, delimiter=delimiter,
                          names=names)

    def getname_val(identifier):
        'return the name and column data for identifier'
        if isinstance(identifier, str):
            return identifier, r[identifier]
        elif isinstance(identifier, Number):
            name = r.dtype.names[int(identifier)]
            return name, r[name]
        else:
            raise TypeError('identifier must be a string or integer')

    xname, x = getname_val(cols[0])
    ynamelist = []

    if len(cols) == 1:
        ax1 = fig.add_subplot(1, 1, 1)
        funcname = plotfuncs.get(cols[0], 'plot')
        func = getattr(ax1, funcname)
        func(x, **kwargs)
        ax1.set_ylabel(xname)
    else:
        N = len(cols)
        for i in range(1, N):
            if subplots:
                if i == 1:
                    ax = ax1 = fig.add_subplot(N - 1, 1, i)
                else:
                    ax = fig.add_subplot(N - 1, 1, i, sharex=ax1)
            elif i == 1:
                ax = fig.add_subplot(1, 1, 1)

            yname, y = getname_val(cols[i])
            ynamelist.append(yname)

            funcname = plotfuncs.get(cols[i], 'plot')
            func = getattr(ax, funcname)

            func(x, y, **kwargs)
            if subplots:
                ax.set_ylabel(yname)
            if ax.is_last_row():
                ax.set_xlabel(xname)
            else:
                ax.set_xlabel('')

    if not subplots:
        ax.legend(ynamelist)

    if xname == 'date':
        fig.autofmt_xdate()


# If rcParams['backend_fallback'] is true, and an interactive backend is
# requested, ignore rcParams['backend'] and force selection of a backend that
# is compatible with the current running interactive framework.
if (rcParams["backend_fallback"]
        and dict.__getitem__(rcParams, "backend") in (
            set(_interactive_bk) - {'WebAgg', 'nbAgg'})
        and _get_running_interactive_framework()):
    dict.__setitem__(rcParams, "backend", rcsetup._auto_backend_sentinel)
# Set up the backend.
switch_backend(rcParams["backend"])

# Just to be safe.  Interactive mode can be turned on without
# calling `plt.ion()` so register it again here.
# This is safe because multiple calls to `install_repl_displayhook`
# are no-ops and the registered function respect `mpl.is_interactive()`
# to determine if they should trigger a draw.
install_repl_displayhook()


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


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Figure.figimage)
def figimage(
        X, xo=0, yo=0, alpha=None, norm=None, cmap=None, vmin=None,
        vmax=None, origin=None, resize=False, **kwargs):
    return gcf().figimage(
        X, xo=xo, yo=yo, alpha=alpha, norm=norm, cmap=cmap, vmin=vmin,
        vmax=vmax, origin=origin, resize=resize, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Figure.text)
def figtext(
        x, y, s, fontdict=None,
        withdash=cbook.deprecation._deprecated_parameter, **kwargs):
    return gcf().text(
        x, y, s, fontdict=fontdict, withdash=withdash, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Figure.ginput)
def ginput(
        n=1, timeout=30, show_clicks=True, mouse_add=1, mouse_pop=3,
        mouse_stop=2):
    return gcf().ginput(
        n=n, timeout=timeout, show_clicks=show_clicks,
        mouse_add=mouse_add, mouse_pop=mouse_pop,
        mouse_stop=mouse_stop)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Figure.suptitle)
def suptitle(t, **kwargs):
    return gcf().suptitle(t, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Figure.waitforbuttonpress)
def waitforbuttonpress(timeout=-1):
    return gcf().waitforbuttonpress(timeout=timeout)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.acorr)
def acorr(x, *, data=None, **kwargs):
    return gca().acorr(
        x, **({"data": data} if data is not None else {}), **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.angle_spectrum)
def angle_spectrum(
        x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, *,
        data=None, **kwargs):
    return gca().angle_spectrum(
        x, Fs=Fs, Fc=Fc, window=window, pad_to=pad_to, sides=sides,
        **({"data": data} if data is not None else {}), **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.annotate)
def annotate(s, xy, *args, **kwargs):
    return gca().annotate(s, xy, *args, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.arrow)
def arrow(x, y, dx, dy, **kwargs):
    return gca().arrow(x, y, dx, dy, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.autoscale)
def autoscale(enable=True, axis='both', tight=None):
    return gca().autoscale(enable=enable, axis=axis, tight=tight)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.axhline)
def axhline(y=0, xmin=0, xmax=1, **kwargs):
    return gca().axhline(y=y, xmin=xmin, xmax=xmax, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.axhspan)
def axhspan(ymin, ymax, xmin=0, xmax=1, **kwargs):
    return gca().axhspan(ymin, ymax, xmin=xmin, xmax=xmax, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.axis)
def axis(*args, emit=True, **kwargs):
    return gca().axis(*args, emit=emit, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.axvline)
def axvline(x=0, ymin=0, ymax=1, **kwargs):
    return gca().axvline(x=x, ymin=ymin, ymax=ymax, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.axvspan)
def axvspan(xmin, xmax, ymin=0, ymax=1, **kwargs):
    return gca().axvspan(xmin, xmax, ymin=ymin, ymax=ymax, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.bar)
def bar(
        x, height, width=0.8, bottom=None, *, align='center',
        data=None, **kwargs):
    return gca().bar(
        x, height, width=width, bottom=bottom, align=align,
        **({"data": data} if data is not None else {}), **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.barbs)
def barbs(*args, data=None, **kw):
    return gca().barbs(
        *args, **({"data": data} if data is not None else {}), **kw)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.barh)
def barh(y, width, height=0.8, left=None, *, align='center', **kwargs):
    return gca().barh(
        y, width, height=height, left=left, align=align, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.boxplot)
def boxplot(
        x, notch=None, sym=None, vert=None, whis=None,
        positions=None, widths=None, patch_artist=None,
        bootstrap=None, usermedians=None, conf_intervals=None,
        meanline=None, showmeans=None, showcaps=None, showbox=None,
        showfliers=None, boxprops=None, labels=None, flierprops=None,
        medianprops=None, meanprops=None, capprops=None,
        whiskerprops=None, manage_ticks=True, autorange=False,
        zorder=None, *, data=None):
    return gca().boxplot(
        x, notch=notch, sym=sym, vert=vert, whis=whis,
        positions=positions, widths=widths, patch_artist=patch_artist,
        bootstrap=bootstrap, usermedians=usermedians,
        conf_intervals=conf_intervals, meanline=meanline,
        showmeans=showmeans, showcaps=showcaps, showbox=showbox,
        showfliers=showfliers, boxprops=boxprops, labels=labels,
        flierprops=flierprops, medianprops=medianprops,
        meanprops=meanprops, capprops=capprops,
        whiskerprops=whiskerprops, manage_ticks=manage_ticks,
        autorange=autorange, zorder=zorder, **({"data": data} if data
        is not None else {}))


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.broken_barh)
def broken_barh(xranges, yrange, *, data=None, **kwargs):
    return gca().broken_barh(
        xranges, yrange, **({"data": data} if data is not None else
        {}), **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.cla)
def cla():
    return gca().cla()


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.clabel)
def clabel(CS, *args, **kwargs):
    return gca().clabel(CS, *args, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.cohere)
def cohere(
        x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
        window=mlab.window_hanning, noverlap=0, pad_to=None,
        sides='default', scale_by_freq=None, *, data=None, **kwargs):
    return gca().cohere(
        x, y, NFFT=NFFT, Fs=Fs, Fc=Fc, detrend=detrend, window=window,
        noverlap=noverlap, pad_to=pad_to, sides=sides,
        scale_by_freq=scale_by_freq, **({"data": data} if data is not
        None else {}), **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.contour)
def contour(*args, data=None, **kwargs):
    __ret = gca().contour(
        *args, **({"data": data} if data is not None else {}),
        **kwargs)
    if __ret._A is not None: sci(__ret)  # noqa
    return __ret


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.contourf)
def contourf(*args, data=None, **kwargs):
    __ret = gca().contourf(
        *args, **({"data": data} if data is not None else {}),
        **kwargs)
    if __ret._A is not None: sci(__ret)  # noqa
    return __ret


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.csd)
def csd(
        x, y, NFFT=None, Fs=None, Fc=None, detrend=None, window=None,
        noverlap=None, pad_to=None, sides=None, scale_by_freq=None,
        return_line=None, *, data=None, **kwargs):
    return gca().csd(
        x, y, NFFT=NFFT, Fs=Fs, Fc=Fc, detrend=detrend, window=window,
        noverlap=noverlap, pad_to=pad_to, sides=sides,
        scale_by_freq=scale_by_freq, return_line=return_line,
        **({"data": data} if data is not None else {}), **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.errorbar)
def errorbar(
        x, y, yerr=None, xerr=None, fmt='', ecolor=None,
        elinewidth=None, capsize=None, barsabove=False, lolims=False,
        uplims=False, xlolims=False, xuplims=False, errorevery=1,
        capthick=None, *, data=None, **kwargs):
    return gca().errorbar(
        x, y, yerr=yerr, xerr=xerr, fmt=fmt, ecolor=ecolor,
        elinewidth=elinewidth, capsize=capsize, barsabove=barsabove,
        lolims=lolims, uplims=uplims, xlolims=xlolims,
        xuplims=xuplims, errorevery=errorevery, capthick=capthick,
        **({"data": data} if data is not None else {}), **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.eventplot)
def eventplot(
        positions, orientation='horizontal', lineoffsets=1,
        linelengths=1, linewidths=None, colors=None,
        linestyles='solid', *, data=None, **kwargs):
    return gca().eventplot(
        positions, orientation=orientation, lineoffsets=lineoffsets,
        linelengths=linelengths, linewidths=linewidths, colors=colors,
        linestyles=linestyles, **({"data": data} if data is not None
        else {}), **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.fill)
def fill(*args, data=None, **kwargs):
    return gca().fill(
        *args, **({"data": data} if data is not None else {}),
        **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.fill_between)
def fill_between(
        x, y1, y2=0, where=None, interpolate=False, step=None, *,
        data=None, **kwargs):
    return gca().fill_between(
        x, y1, y2=y2, where=where, interpolate=interpolate, step=step,
        **({"data": data} if data is not None else {}), **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.fill_betweenx)
def fill_betweenx(
        y, x1, x2=0, where=None, step=None, interpolate=False, *,
        data=None, **kwargs):
    return gca().fill_betweenx(
        y, x1, x2=x2, where=where, step=step, interpolate=interpolate,
        **({"data": data} if data is not None else {}), **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.grid)
def grid(b=None, which='major', axis='both', **kwargs):
    return gca().grid(b=b, which=which, axis=axis, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.hexbin)
def hexbin(
        x, y, C=None, gridsize=100, bins=None, xscale='linear',
        yscale='linear', extent=None, cmap=None, norm=None, vmin=None,
        vmax=None, alpha=None, linewidths=None, edgecolors='face',
        reduce_C_function=np.mean, mincnt=None, marginals=False, *,
        data=None, **kwargs):
    __ret = gca().hexbin(
        x, y, C=C, gridsize=gridsize, bins=bins, xscale=xscale,
        yscale=yscale, extent=extent, cmap=cmap, norm=norm, vmin=vmin,
        vmax=vmax, alpha=alpha, linewidths=linewidths,
        edgecolors=edgecolors, reduce_C_function=reduce_C_function,
        mincnt=mincnt, marginals=marginals, **({"data": data} if data
        is not None else {}), **kwargs)
    sci(__ret)
    return __ret


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.hist)
def hist(
        x, bins=None, range=None, density=False, weights=None,
        cumulative=False, bottom=None, histtype='bar', align='mid',
        orientation='vertical', rwidth=None, log=False, color=None,
        label=None, stacked=False, *, data=None, **kwargs):
    return gca().hist(
        x, bins=bins, range=range, density=density, weights=weights,
        cumulative=cumulative, bottom=bottom, histtype=histtype,
        align=align, orientation=orientation, rwidth=rwidth, log=log,
        color=color, label=label, stacked=stacked, **({"data": data}
        if data is not None else {}), **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.hist2d)
def hist2d(
        x, y, bins=10, range=None, density=False, weights=None,
        cmin=None, cmax=None, *, data=None, **kwargs):
    __ret = gca().hist2d(
        x, y, bins=bins, range=range, density=density,
        weights=weights, cmin=cmin, cmax=cmax, **({"data": data} if
        data is not None else {}), **kwargs)
    sci(__ret[-1])
    return __ret


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.hlines)
def hlines(
        y, xmin, xmax, colors='k', linestyles='solid', label='', *,
        data=None, **kwargs):
    return gca().hlines(
        y, xmin, xmax, colors=colors, linestyles=linestyles,
        label=label, **({"data": data} if data is not None else {}),
        **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.imshow)
def imshow(
        X, cmap=None, norm=None, aspect=None, interpolation=None,
        alpha=None, vmin=None, vmax=None, origin=None, extent=None,
        shape=cbook.deprecation._deprecated_parameter, filternorm=1,
        filterrad=4.0, imlim=cbook.deprecation._deprecated_parameter,
        resample=None, url=None, *, data=None, **kwargs):
    __ret = gca().imshow(
        X, cmap=cmap, norm=norm, aspect=aspect,
        interpolation=interpolation, alpha=alpha, vmin=vmin,
        vmax=vmax, origin=origin, extent=extent, shape=shape,
        filternorm=filternorm, filterrad=filterrad, imlim=imlim,
        resample=resample, url=url, **({"data": data} if data is not
        None else {}), **kwargs)
    sci(__ret)
    return __ret


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.legend)
def legend(*args, **kwargs):
    return gca().legend(*args, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.locator_params)
def locator_params(axis='both', tight=None, **kwargs):
    return gca().locator_params(axis=axis, tight=tight, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.loglog)
def loglog(*args, **kwargs):
    return gca().loglog(*args, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.magnitude_spectrum)
def magnitude_spectrum(
        x, Fs=None, Fc=None, window=None, pad_to=None, sides=None,
        scale=None, *, data=None, **kwargs):
    return gca().magnitude_spectrum(
        x, Fs=Fs, Fc=Fc, window=window, pad_to=pad_to, sides=sides,
        scale=scale, **({"data": data} if data is not None else {}),
        **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.margins)
def margins(*margins, x=None, y=None, tight=True):
    return gca().margins(*margins, x=x, y=y, tight=tight)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.minorticks_off)
def minorticks_off():
    return gca().minorticks_off()


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.minorticks_on)
def minorticks_on():
    return gca().minorticks_on()


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.pcolor)
def pcolor(
        *args, alpha=None, norm=None, cmap=None, vmin=None,
        vmax=None, data=None, **kwargs):
    __ret = gca().pcolor(
        *args, alpha=alpha, norm=norm, cmap=cmap, vmin=vmin,
        vmax=vmax, **({"data": data} if data is not None else {}),
        **kwargs)
    sci(__ret)
    return __ret


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.pcolormesh)
def pcolormesh(
        *args, alpha=None, norm=None, cmap=None, vmin=None,
        vmax=None, shading='flat', antialiased=False, data=None,
        **kwargs):
    __ret = gca().pcolormesh(
        *args, alpha=alpha, norm=norm, cmap=cmap, vmin=vmin,
        vmax=vmax, shading=shading, antialiased=antialiased,
        **({"data": data} if data is not None else {}), **kwargs)
    sci(__ret)
    return __ret


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.phase_spectrum)
def phase_spectrum(
        x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, *,
        data=None, **kwargs):
    return gca().phase_spectrum(
        x, Fs=Fs, Fc=Fc, window=window, pad_to=pad_to, sides=sides,
        **({"data": data} if data is not None else {}), **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.pie)
def pie(
        x, explode=None, labels=None, colors=None, autopct=None,
        pctdistance=0.6, shadow=False, labeldistance=1.1,
        startangle=None, radius=None, counterclock=True,
        wedgeprops=None, textprops=None, center=(0, 0), frame=False,
        rotatelabels=False, *, data=None):
    return gca().pie(
        x, explode=explode, labels=labels, colors=colors,
        autopct=autopct, pctdistance=pctdistance, shadow=shadow,
        labeldistance=labeldistance, startangle=startangle,
        radius=radius, counterclock=counterclock,
        wedgeprops=wedgeprops, textprops=textprops, center=center,
        frame=frame, rotatelabels=rotatelabels, **({"data": data} if
        data is not None else {}))


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.plot)
def plot(*args, scalex=True, scaley=True, data=None, **kwargs):
    return gca().plot(
        *args, scalex=scalex, scaley=scaley, **({"data": data} if data
        is not None else {}), **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.plot_date)
def plot_date(
        x, y, fmt='o', tz=None, xdate=True, ydate=False, *,
        data=None, **kwargs):
    return gca().plot_date(
        x, y, fmt=fmt, tz=tz, xdate=xdate, ydate=ydate, **({"data":
        data} if data is not None else {}), **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.psd)
def psd(
        x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None,
        noverlap=None, pad_to=None, sides=None, scale_by_freq=None,
        return_line=None, *, data=None, **kwargs):
    return gca().psd(
        x, NFFT=NFFT, Fs=Fs, Fc=Fc, detrend=detrend, window=window,
        noverlap=noverlap, pad_to=pad_to, sides=sides,
        scale_by_freq=scale_by_freq, return_line=return_line,
        **({"data": data} if data is not None else {}), **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.quiver)
def quiver(*args, data=None, **kw):
    __ret = gca().quiver(
        *args, **({"data": data} if data is not None else {}), **kw)
    sci(__ret)
    return __ret


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.quiverkey)
def quiverkey(Q, X, Y, U, label, **kw):
    return gca().quiverkey(Q, X, Y, U, label, **kw)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.scatter)
def scatter(
        x, y, s=None, c=None, marker=None, cmap=None, norm=None,
        vmin=None, vmax=None, alpha=None, linewidths=None,
        verts=cbook.deprecation._deprecated_parameter,
        edgecolors=None, *, plotnonfinite=False, data=None, **kwargs):
    __ret = gca().scatter(
        x, y, s=s, c=c, marker=marker, cmap=cmap, norm=norm,
        vmin=vmin, vmax=vmax, alpha=alpha, linewidths=linewidths,
        verts=verts, edgecolors=edgecolors,
        plotnonfinite=plotnonfinite, **({"data": data} if data is not
        None else {}), **kwargs)
    sci(__ret)
    return __ret


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.semilogx)
def semilogx(*args, **kwargs):
    return gca().semilogx(*args, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.semilogy)
def semilogy(*args, **kwargs):
    return gca().semilogy(*args, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.specgram)
def specgram(
        x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None,
        noverlap=None, cmap=None, xextent=None, pad_to=None,
        sides=None, scale_by_freq=None, mode=None, scale=None,
        vmin=None, vmax=None, *, data=None, **kwargs):
    __ret = gca().specgram(
        x, NFFT=NFFT, Fs=Fs, Fc=Fc, detrend=detrend, window=window,
        noverlap=noverlap, cmap=cmap, xextent=xextent, pad_to=pad_to,
        sides=sides, scale_by_freq=scale_by_freq, mode=mode,
        scale=scale, vmin=vmin, vmax=vmax, **({"data": data} if data
        is not None else {}), **kwargs)
    sci(__ret[-1])
    return __ret


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.spy)
def spy(
        Z, precision=0, marker=None, markersize=None, aspect='equal',
        origin='upper', **kwargs):
    __ret = gca().spy(
        Z, precision=precision, marker=marker, markersize=markersize,
        aspect=aspect, origin=origin, **kwargs)
    if isinstance(__ret, cm.ScalarMappable): sci(__ret)  # noqa
    return __ret


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.stackplot)
def stackplot(
        x, *args, labels=(), colors=None, baseline='zero', data=None,
        **kwargs):
    return gca().stackplot(
        x, *args, labels=labels, colors=colors, baseline=baseline,
        **({"data": data} if data is not None else {}), **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.stem)
def stem(
        *args, linefmt=None, markerfmt=None, basefmt=None, bottom=0,
        label=None, use_line_collection=False, data=None):
    return gca().stem(
        *args, linefmt=linefmt, markerfmt=markerfmt, basefmt=basefmt,
        bottom=bottom, label=label,
        use_line_collection=use_line_collection, **({"data": data} if
        data is not None else {}))


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.step)
def step(x, y, *args, where='pre', data=None, **kwargs):
    return gca().step(
        x, y, *args, where=where, **({"data": data} if data is not
        None else {}), **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.streamplot)
def streamplot(
        x, y, u, v, density=1, linewidth=None, color=None, cmap=None,
        norm=None, arrowsize=1, arrowstyle='-|>', minlength=0.1,
        transform=None, zorder=None, start_points=None, maxlength=4.0,
        integration_direction='both', *, data=None):
    __ret = gca().streamplot(
        x, y, u, v, density=density, linewidth=linewidth, color=color,
        cmap=cmap, norm=norm, arrowsize=arrowsize,
        arrowstyle=arrowstyle, minlength=minlength,
        transform=transform, zorder=zorder, start_points=start_points,
        maxlength=maxlength,
        integration_direction=integration_direction, **({"data": data}
        if data is not None else {}))
    sci(__ret.lines)
    return __ret


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.table)
def table(
        cellText=None, cellColours=None, cellLoc='right',
        colWidths=None, rowLabels=None, rowColours=None,
        rowLoc='left', colLabels=None, colColours=None,
        colLoc='center', loc='bottom', bbox=None, edges='closed',
        **kwargs):
    return gca().table(
        cellText=cellText, cellColours=cellColours, cellLoc=cellLoc,
        colWidths=colWidths, rowLabels=rowLabels,
        rowColours=rowColours, rowLoc=rowLoc, colLabels=colLabels,
        colColours=colColours, colLoc=colLoc, loc=loc, bbox=bbox,
        edges=edges, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.text)
def text(
        x, y, s, fontdict=None,
        withdash=cbook.deprecation._deprecated_parameter, **kwargs):
    return gca().text(x, y, s, fontdict=fontdict, withdash=withdash, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.tick_params)
def tick_params(axis='both', **kwargs):
    return gca().tick_params(axis=axis, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.ticklabel_format)
def ticklabel_format(
        *, axis='both', style='', scilimits=None, useOffset=None,
        useLocale=None, useMathText=None):
    return gca().ticklabel_format(
        axis=axis, style=style, scilimits=scilimits,
        useOffset=useOffset, useLocale=useLocale,
        useMathText=useMathText)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.tricontour)
def tricontour(*args, **kwargs):
    __ret = gca().tricontour(*args, **kwargs)
    if __ret._A is not None: sci(__ret)  # noqa
    return __ret


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.tricontourf)
def tricontourf(*args, **kwargs):
    __ret = gca().tricontourf(*args, **kwargs)
    if __ret._A is not None: sci(__ret)  # noqa
    return __ret


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.tripcolor)
def tripcolor(
        *args, alpha=1.0, norm=None, cmap=None, vmin=None, vmax=None,
        shading='flat', facecolors=None, **kwargs):
    __ret = gca().tripcolor(
        *args, alpha=alpha, norm=norm, cmap=cmap, vmin=vmin,
        vmax=vmax, shading=shading, facecolors=facecolors, **kwargs)
    sci(__ret)
    return __ret


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.triplot)
def triplot(*args, **kwargs):
    return gca().triplot(*args, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.violinplot)
def violinplot(
        dataset, positions=None, vert=True, widths=0.5,
        showmeans=False, showextrema=True, showmedians=False,
        quantiles=None, points=100, bw_method=None, *, data=None):
    return gca().violinplot(
        dataset, positions=positions, vert=vert, widths=widths,
        showmeans=showmeans, showextrema=showextrema,
        showmedians=showmedians, quantiles=quantiles, points=points,
        bw_method=bw_method, **({"data": data} if data is not None
        else {}))


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.vlines)
def vlines(
        x, ymin, ymax, colors='k', linestyles='solid', label='', *,
        data=None, **kwargs):
    return gca().vlines(
        x, ymin, ymax, colors=colors, linestyles=linestyles,
        label=label, **({"data": data} if data is not None else {}),
        **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.xcorr)
def xcorr(
        x, y, normed=True, detrend=mlab.detrend_none, usevlines=True,
        maxlags=10, *, data=None, **kwargs):
    return gca().xcorr(
        x, y, normed=normed, detrend=detrend, usevlines=usevlines,
        maxlags=maxlags, **({"data": data} if data is not None else
        {}), **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes._sci)
def sci(im):
    return gca()._sci(im)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.set_title)
def title(label, fontdict=None, loc=None, pad=None, **kwargs):
    return gca().set_title(
        label, fontdict=fontdict, loc=loc, pad=pad, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.set_xlabel)
def xlabel(xlabel, fontdict=None, labelpad=None, **kwargs):
    return gca().set_xlabel(
        xlabel, fontdict=fontdict, labelpad=labelpad, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.set_ylabel)
def ylabel(ylabel, fontdict=None, labelpad=None, **kwargs):
    return gca().set_ylabel(
        ylabel, fontdict=fontdict, labelpad=labelpad, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.set_xscale)
def xscale(value, **kwargs):
    return gca().set_xscale(value, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@docstring.copy(Axes.set_yscale)
def yscale(value, **kwargs):
    return gca().set_yscale(value, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
def autumn():
    """
    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.
    """
    set_cmap("autumn")


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
def bone():
    """
    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.
    """
    set_cmap("bone")


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
def cool():
    """
    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.
    """
    set_cmap("cool")


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
def copper():
    """
    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.
    """
    set_cmap("copper")


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
def flag():
    """
    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.
    """
    set_cmap("flag")


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
def gray():
    """
    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.
    """
    set_cmap("gray")


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
def hot():
    """
    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.
    """
    set_cmap("hot")


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
def hsv():
    """
    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.
    """
    set_cmap("hsv")


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
def jet():
    """
    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.
    """
    set_cmap("jet")


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
def pink():
    """
    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.
    """
    set_cmap("pink")


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
def prism():
    """
    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.
    """
    set_cmap("prism")


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
def spring():
    """
    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.
    """
    set_cmap("spring")


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
def summer():
    """
    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.
    """
    set_cmap("summer")


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
def winter():
    """
    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.
    """
    set_cmap("winter")


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
def magma():
    """
    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.
    """
    set_cmap("magma")


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
def inferno():
    """
    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.
    """
    set_cmap("inferno")


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
def plasma():
    """
    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.
    """
    set_cmap("plasma")


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
def viridis():
    """
    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.
    """
    set_cmap("viridis")


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
def nipy_spectral():
    """
    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.
    """
    set_cmap("nipy_spectral")

_setup_pyplot_info_docstrings()