# Note: The first part of this file can be modified in place, but the latter
# part is autogenerated by the boilerplate.py script.

"""
`matplotlib.pyplot` is a state-based interface to matplotlib. It provides
a MATLAB-like way of plotting.

pyplot is mainly intended for interactive plots and simple cases of
programmatic plot generation::

    import numpy as np
    import matplotlib.pyplot as plt

    x = np.arange(0, 5, 0.1)
    y = np.sin(x)
    plt.plot(x, y)

The object-oriented API is recommended for more complex plots.
"""

import functools
import importlib
import inspect
import logging
from numbers import Number
import re
import sys
import time
try:
    import threading
except ImportError:
    import dummy_threading as threading

from cycler import cycler
import matplotlib
import matplotlib.colorbar
import matplotlib.image
from matplotlib import _api
from matplotlib import rcsetup, style
from matplotlib import _pylab_helpers, interactive
from matplotlib import cbook
from matplotlib import docstring
from matplotlib.backend_bases import FigureCanvasBase, MouseButton
from matplotlib.figure import Figure, figaspect
from matplotlib.gridspec import GridSpec, SubplotSpec
from matplotlib import rcParams, rcParamsDefault, get_backend, rcParamsOrig
from matplotlib.rcsetup import interactive_bk as _interactive_bk
from matplotlib.artist import Artist
from matplotlib.axes import Axes, Subplot
from matplotlib.projections import PolarAxes
from matplotlib import mlab  # for detrend_none, window_hanning
from matplotlib.scale import get_scale_names

from matplotlib import cm
from matplotlib.cm import get_cmap, register_cmap

import numpy as np

# We may not need the following imports here:
from matplotlib.colors import Normalize
from matplotlib.lines import Line2D
from matplotlib.text import Text, Annotation
from matplotlib.patches import Polygon, Rectangle, Circle, Arrow
from matplotlib.widgets import SubplotTool, Button, Slider, Widget

from .ticker import (
    TickHelper, Formatter, FixedFormatter, NullFormatter, FuncFormatter,
    FormatStrFormatter, ScalarFormatter, LogFormatter, LogFormatterExponent,
    LogFormatterMathtext, Locator, IndexLocator, FixedLocator, NullLocator,
    LinearLocator, LogLocator, AutoLocator, MultipleLocator, MaxNLocator)

_log = logging.getLogger(__name__)


_code_objs = {
    _api.rename_parameter:
        _api.rename_parameter("", "old", "new", lambda new: None).__code__,
    _api.make_keyword_only:
        _api.make_keyword_only("", "p", lambda p: None).__code__,
}


def _copy_docstring_and_deprecators(method, func=None):
    if func is None:
        return functools.partial(_copy_docstring_and_deprecators, method)
    decorators = [docstring.copy(method)]
    # Check whether the definition of *method* includes @_api.rename_parameter
    # or @_api.make_keyword_only decorators; if so, propagate them to the
    # pyplot wrapper as well.
    while getattr(method, "__wrapped__", None) is not None:
        for decorator_maker, code in _code_objs.items():
            if method.__code__ is code:
                kwargs = {
                    k: v.cell_contents
                    for k, v in zip(code.co_freevars, method.__closure__)}
                assert kwargs["func"] is method.__wrapped__
                kwargs.pop("func")
                decorators.append(decorator_maker(**kwargs))
        method = method.__wrapped__
    for decorator in decorators[::-1]:
        func = decorator(func)
    return func


## Global ##


_IP_REGISTERED = None
_INSTALL_FIG_OBSERVER = False


def install_repl_displayhook():
    """
    Install a repl display hook so that any stale figure are automatically
    redrawn when control is returned to the repl.

    This works both with IPython and with vanilla python shells.
    """
    global _IP_REGISTERED
    global _INSTALL_FIG_OBSERVER

    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 as err:
            raise NotImplementedError("Can not unregister events "
                                      "in IPython < 2.0") from err
        _IP_REGISTERED = None

    if _INSTALL_FIG_OBSERVER:
        _INSTALL_FIG_OBSERVER = False


draw_all = _pylab_helpers.Gcf.draw_all


@functools.wraps(matplotlib.set_loglevel)
def set_loglevel(*args, **kwargs):  # Ensure this appears in the pyplot docs.
    return matplotlib.set_loglevel(*args, **kwargs)


@_copy_docstring_and_deprecators(Artist.findobj)
def findobj(o=None, match=None, include_self=True):
    if o is None:
        o = gcf()
    return o.findobj(match, include_self=include_self)


def _get_required_interactive_framework(backend_mod):
    return getattr(
        backend_mod.FigureCanvas, "required_interactive_framework", None)


def switch_backend(newbackend):
    """
    Close all open figures and set the Matplotlib backend.

    The argument is case-insensitive.  Switching to an interactive backend is
    possible only if no event loop for another interactive backend has started.
    Switching to and from non-interactive backends is always possible.

    Parameters
    ----------
    newbackend : str
        The name of the backend to use.
    """
    global _backend_mod
    # make sure the init is pulled up so we can assign to it later
    import matplotlib.backends
    close("all")

    if newbackend is rcsetup._auto_backend_sentinel:
        current_framework = cbook._get_running_interactive_framework()
        mapping = {'qt5': 'qt5agg',
                   'qt4': 'qt4agg',
                   'gtk3': 'gtk3agg',
                   'wx': 'wxagg',
                   'tk': 'tkagg',
                   'macosx': 'macosx',
                   'headless': 'agg'}

        best_guess = mapping.get(current_framework, None)
        if best_guess is not None:
            candidates = [best_guess]
        else:
            candidates = []
        candidates += ["macosx", "qt5agg", "gtk3agg", "tkagg", "wxagg"]

        # Don't try to fallback on the cairo-based backends as they each have
        # an additional dependency (pycairo) over the agg-based backend, and
        # are of worse quality.
        for candidate in candidates:
            try:
                switch_backend(candidate)
            except ImportError:
                continue
            else:
                rcParamsOrig['backend'] = candidate
                return
        else:
            # Switching to Agg should always succeed; if it doesn't, let the
            # exception propagate out.
            switch_backend("agg")
            rcParamsOrig["backend"] = "agg"
            return

    # Backends are implemented as modules, but "inherit" default method
    # implementations from backend_bases._Backend.  This is achieved by
    # creating a "class" that inherits from backend_bases._Backend and whose
    # body is filled with the module's globals.

    backend_name = cbook._backend_module_name(newbackend)

    class backend_mod(matplotlib.backend_bases._Backend):
        locals().update(vars(importlib.import_module(backend_name)))

    required_framework = _get_required_interactive_framework(backend_mod)
    if required_framework is not None:
        current_framework = cbook._get_running_interactive_framework()
        if (current_framework and required_framework
                and current_framework != required_framework):
            raise ImportError(
                "Cannot load backend {!r} which requires the {!r} interactive "
                "framework, as {!r} is currently running".format(
                    newbackend, required_framework, current_framework))

    _log.debug("Loaded backend %s version %s.",
               newbackend, backend_mod.backend_version)

    rcParams['backend'] = rcParamsDefault['backend'] = newbackend
    _backend_mod = backend_mod
    for func_name in ["new_figure_manager", "draw_if_interactive", "show"]:
        globals()[func_name].__signature__ = inspect.signature(
            getattr(backend_mod, func_name))

    # Need to keep a global reference to the backend for compatibility reasons.
    # See https://github.com/matplotlib/matplotlib/issues/6092
    matplotlib.backends.backend = newbackend


def _warn_if_gui_out_of_main_thread():
    if (_get_required_interactive_framework(_backend_mod)
            and threading.current_thread() is not threading.main_thread()):
        _api.warn_external(
            "Starting a Matplotlib GUI outside of the main thread will likely "
            "fail.")


# This function's signature is rewritten upon backend-load by switch_backend.
def new_figure_manager(*args, **kwargs):
    """Create a new figure manager instance."""
    _warn_if_gui_out_of_main_thread()
    return _backend_mod.new_figure_manager(*args, **kwargs)


# This function's signature is rewritten upon backend-load by switch_backend.
def draw_if_interactive(*args, **kwargs):
    """
    Redraw the current figure if in interactive mode.

    .. warning::

        End users will typically not have to call this function because the
        the interactive mode takes care of this.
    """
    return _backend_mod.draw_if_interactive(*args, **kwargs)


# This function's signature is rewritten upon backend-load by switch_backend.
def show(*args, **kwargs):
    """
    Display all open figures.

    Parameters
    ----------
    block : bool, optional
        Whether to wait for all figures to be closed before returning.

        If `True` block and run the GUI main loop until all figure windows
        are closed.

        If `False` ensure that all figure windows are displayed and return
        immediately.  In this case, you are responsible for ensuring
        that the event loop is running to have responsive figures.

        Defaults to True in non-interactive mode and to False in interactive
        mode (see `.pyplot.isinteractive`).

    See Also
    --------
    ion : Enable interactive mode, which shows / updates the figure after
          every plotting command, so that calling ``show()`` is not necessary.
    ioff : Disable interactive mode.
    savefig : Save the figure to an image file instead of showing it on screen.

    Notes
    -----
    **Saving figures to file and showing a window at the same time**

    If you want an image file as well as a user interface window, use
    `.pyplot.savefig` before `.pyplot.show`. At the end of (a blocking)
    ``show()`` the figure is closed and thus unregistered from pyplot. Calling
    `.pyplot.savefig` afterwards would save a new and thus empty figure. This
    limitation of command order does not apply if the show is non-blocking or
    if you keep a reference to the figure and use `.Figure.savefig`.

    **Auto-show in jupyter notebooks**

    The jupyter backends (activated via ``%matplotlib inline``,
    ``%matplotlib notebook``, or ``%matplotlib widget``), call ``show()`` at
    the end of every cell by default. Thus, you usually don't have to call it
    explicitly there.
    """
    _warn_if_gui_out_of_main_thread()
    return _backend_mod.show(*args, **kwargs)


def isinteractive():
    """
    Return whether plots are updated after every plotting command.

    The interactive mode is mainly useful if you build plots from the command
    line and want to see the effect of each command while you are building the
    figure.

    In interactive mode:

    - newly created figures will be shown immediately;
    - figures will automatically redraw on change;
    - `.pyplot.show` will not block by default.

    In non-interactive mode:

    - newly created figures and changes to figures will not be reflected until
      explicitly asked to be;
    - `.pyplot.show` will block by default.

    See Also
    --------
    ion : Enable interactive mode.
    ioff : Disable interactive mode.
    show : Show all figures (and maybe block).
    pause : Show all figures, and block for a time.
    """
    return matplotlib.is_interactive()


class _IoffContext:
    """
    Context manager for `.ioff`.

    The state is changed in ``__init__()`` instead of ``__enter__()``. The
    latter is a no-op. This allows using `.ioff` both as a function and
    as a context.
    """

    def __init__(self):
        self.wasinteractive = isinteractive()
        matplotlib.interactive(False)
        uninstall_repl_displayhook()

    def __enter__(self):
        pass

    def __exit__(self, exc_type, exc_value, traceback):
        if self.wasinteractive:
            matplotlib.interactive(True)
            install_repl_displayhook()
        else:
            matplotlib.interactive(False)
            uninstall_repl_displayhook()


class _IonContext:
    """
    Context manager for `.ion`.

    The state is changed in ``__init__()`` instead of ``__enter__()``. The
    latter is a no-op. This allows using `.ion` both as a function and
    as a context.
    """

    def __init__(self):
        self.wasinteractive = isinteractive()
        matplotlib.interactive(True)
        install_repl_displayhook()

    def __enter__(self):
        pass

    def __exit__(self, exc_type, exc_value, traceback):
        if not self.wasinteractive:
            matplotlib.interactive(False)
            uninstall_repl_displayhook()
        else:
            matplotlib.interactive(True)
            install_repl_displayhook()


def ioff():
    """
    Disable interactive mode.

    See `.pyplot.isinteractive` for more details.

    See Also
    --------
    ion : Enable interactive mode.
    isinteractive : Whether interactive mode is enabled.
    show : Show all figures (and maybe block).
    pause : Show all figures, and block for a time.

    Notes
    -----
    For a temporary change, this can be used as a context manager::

        # if interactive mode is on
        # then figures will be shown on creation
        plt.ion()
        # This figure will be shown immediately
        fig = plt.figure()

        with plt.ioff():
            # interactive mode will be off
            # figures will not automatically be shown
            fig2 = plt.figure()
            # ...

    To enable usage as a context manager, this function returns an
    ``_IoffContext`` object. The return value is not intended to be stored
    or accessed by the user.
    """
    return _IoffContext()


def ion():
    """
    Enable interactive mode.

    See `.pyplot.isinteractive` for more details.

    See Also
    --------
    ioff : Disable interactive mode.
    isinteractive : Whether interactive mode is enabled.
    show : Show all figures (and maybe block).
    pause : Show all figures, and block for a time.

    Notes
    -----
    For a temporary change, this can be used as a context manager::

        # if interactive mode is off
        # then figures will not be shown on creation
        plt.ioff()
        # This figure will not be shown immediately
        fig = plt.figure()

        with plt.ion():
            # interactive mode will be on
            # figures will automatically be shown
            fig2 = plt.figure()
            # ...

    To enable usage as a context manager, this function returns an
    ``_IonContext`` object. The return value is not intended to be stored
    or accessed by the user.
    """
    return _IonContext()


def pause(interval):
    """
    Run the GUI event loop for *interval* seconds.

    If there is an active figure, it will be updated and displayed before the
    pause, and the GUI event loop (if any) will run during the pause.

    This can be used for crude animation.  For more complex animation use
    :mod:`matplotlib.animation`.

    If there is no active figure, sleep for *interval* seconds instead.

    See Also
    --------
    matplotlib.animation : Proper animations
    show : Show all figures and optional block until all figures are closed.
    """
    manager = _pylab_helpers.Gcf.get_active()
    if manager is not None:
        canvas = manager.canvas
        if canvas.figure.stale:
            canvas.draw_idle()
        show(block=False)
        canvas.start_event_loop(interval)
    else:
        time.sleep(interval)


@_copy_docstring_and_deprecators(matplotlib.rc)
def rc(group, **kwargs):
    matplotlib.rc(group, **kwargs)


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


@_copy_docstring_and_deprecators(matplotlib.rcdefaults)
def rcdefaults():
    matplotlib.rcdefaults()
    if matplotlib.is_interactive():
        draw_all()


# getp/get/setp are explicitly reexported so that they show up in pyplot docs.


@_copy_docstring_and_deprecators(matplotlib.artist.getp)
def getp(obj, *args, **kwargs):
    return matplotlib.artist.getp(obj, *args, **kwargs)


@_copy_docstring_and_deprecators(matplotlib.artist.get)
def get(obj, *args, **kwargs):
    return matplotlib.artist.get(obj, *args, **kwargs)


@_copy_docstring_and_deprecators(matplotlib.artist.setp)
def setp(obj, *args, **kwargs):
    return matplotlib.artist.setp(obj, *args, **kwargs)


def xkcd(scale=1, length=100, randomness=2):
    """
    Turn on `xkcd <https://xkcd.com/>`_ sketch-style drawing mode.  This will
    only have effect on things drawn after this function is called.

    For best results, the "Humor Sans" font should be installed: it is
    not included with Matplotlib.

    Parameters
    ----------
    scale : float, optional
        The amplitude of the wiggle perpendicular to the source line.
    length : float, optional
        The length of the wiggle along the line.
    randomness : float, optional
        The scale factor by which the length is shrunken or expanded.

    Notes
    -----
    This function works by a number of rcParams, so it will probably
    override others you have set before.

    If you want the effects of this function to be temporary, it can
    be used as a context manager, for example::

        with plt.xkcd():
            # This figure will be in XKCD-style
            fig1 = plt.figure()
            # ...

        # This figure will be in regular style
        fig2 = plt.figure()
    """
    return _xkcd(scale, length, randomness)


class _xkcd:
    # This cannot be implemented in terms of rc_context() because this needs to
    # work as a non-contextmanager too.

    def __init__(self, scale, length, randomness):
        self._orig = rcParams.copy()

        if rcParams['text.usetex']:
            raise RuntimeError(
                "xkcd mode is not compatible with text.usetex = True")

        from matplotlib import patheffects
        rcParams.update({
            'font.family': ['xkcd', 'xkcd Script', 'Humor Sans', 'Comic Neue',
                            'Comic Sans MS'],
            'font.size': 14.0,
            'path.sketch': (scale, length, randomness),
            'path.effects': [
                patheffects.withStroke(linewidth=4, foreground="w")],
            'axes.linewidth': 1.5,
            'lines.linewidth': 2.0,
            'figure.facecolor': 'white',
            'grid.linewidth': 0.0,
            'axes.grid': False,
            'axes.unicode_minus': False,
            'axes.edgecolor': 'black',
            'xtick.major.size': 8,
            'xtick.major.width': 3,
            'ytick.major.size': 8,
            'ytick.major.width': 3,
        })

    def __enter__(self):
        return self

    def __exit__(self, *args):
        dict.update(rcParams, self._orig)


## Figures ##

def figure(num=None,  # autoincrement if None, else integer from 1-N
           figsize=None,  # defaults to rc figure.figsize
           dpi=None,  # defaults to rc figure.dpi
           facecolor=None,  # defaults to rc figure.facecolor
           edgecolor=None,  # defaults to rc figure.edgecolor
           frameon=True,
           FigureClass=Figure,
           clear=False,
           **kwargs
           ):
    """
    Create a new figure, or activate an existing figure.

    Parameters
    ----------
    num : int or str or `.Figure`, optional
        A unique identifier for the figure.

        If a figure with that identifier already exists, this figure is made
        active and returned. An integer refers to the ``Figure.number``
        attribute, a string refers to the figure label.

        If there is no figure with the identifier or *num* is not given, a new
        figure is created, made active and returned.  If *num* is an int, it
        will be used for the ``Figure.number`` attribute, otherwise, an
        auto-generated integer value is used (starting at 1 and incremented
        for each new figure). If *num* is a string, the figure label and the
        window title is set to this value.

    figsize : (float, float), default: :rc:`figure.figsize`
        Width, height in inches.

    dpi : float, default: :rc:`figure.dpi`
        The resolution of the figure in dots-per-inch.

    facecolor : color, default: :rc:`figure.facecolor`
        The background color.

    edgecolor : color, default: :rc:`figure.edgecolor`
        The border color.

    frameon : bool, default: True
        If False, suppress drawing the figure frame.

    FigureClass : subclass of `~matplotlib.figure.Figure`
        Optionally use a custom `.Figure` instance.

    clear : bool, default: False
        If True and the figure already exists, then it is cleared.

    tight_layout : bool or dict, default: :rc:`figure.autolayout`
        If ``False`` use *subplotpars*. If ``True`` adjust subplot
        parameters using `.tight_layout` with default padding.
        When providing a dict containing the keys ``pad``, ``w_pad``,
        ``h_pad``, and ``rect``, the default `.tight_layout` paddings
        will be overridden.

    constrained_layout : bool, default: :rc:`figure.constrained_layout.use`
        If ``True`` use constrained layout to adjust positioning of plot
        elements.  Like ``tight_layout``, but designed to be more
        flexible.  See
        :doc:`/tutorials/intermediate/constrainedlayout_guide`
        for examples.  (Note: does not work with `add_subplot` or
        `~.pyplot.subplot2grid`.)


    **kwargs : optional
        See `~.matplotlib.figure.Figure` for other possible arguments.

    Returns
    -------
    `~matplotlib.figure.Figure`
        The `.Figure` instance returned will also be passed to
        new_figure_manager in the backends, which allows to hook custom
        `.Figure` classes into the pyplot interface. Additional kwargs will be
        passed to the `.Figure` init function.

    Notes
    -----
    If you are creating many figures, make sure you explicitly call
    `.pyplot.close` on the figures you are not using, because this will
    enable pyplot to properly clean up the memory.

    `~matplotlib.rcParams` defines the default values, which can be modified
    in the matplotlibrc file.
    """
    if isinstance(num, Figure):
        if num.canvas.manager is None:
            raise ValueError("The passed figure is not managed by pyplot")
        _pylab_helpers.Gcf.set_active(num.canvas.manager)
        return num

    allnums = get_fignums()
    next_num = max(allnums) + 1 if allnums else 1
    fig_label = ''
    if num is None:
        num = next_num
    elif isinstance(num, str):
        fig_label = num
        all_labels = get_figlabels()
        if fig_label not in all_labels:
            if fig_label == 'all':
                _api.warn_external("close('all') closes all existing figures.")
            num = next_num
        else:
            inum = all_labels.index(fig_label)
            num = allnums[inum]
    else:
        num = int(num)  # crude validation of num argument

    manager = _pylab_helpers.Gcf.get_fig_manager(num)
    if manager is None:
        max_open_warning = rcParams['figure.max_open_warning']
        if len(allnums) == max_open_warning >= 1:
            _api.warn_external(
                f"More than {max_open_warning} figures have been opened. "
                f"Figures created through the pyplot interface "
                f"(`matplotlib.pyplot.figure`) are retained until explicitly "
                f"closed and may consume too much memory. (To control this "
                f"warning, see the rcParam `figure.max_open_warning`).",
                RuntimeWarning)

        manager = new_figure_manager(
            num, figsize=figsize, dpi=dpi,
            facecolor=facecolor, edgecolor=edgecolor, frameon=frameon,
            FigureClass=FigureClass, **kwargs)
        fig = manager.canvas.figure
        if fig_label:
            fig.set_label(fig_label)

        _pylab_helpers.Gcf._set_new_active_manager(manager)

        # make sure backends (inline) that we don't ship that expect this
        # to be called in plotting commands to make the figure call show
        # still work.  There is probably a better way to do this in the
        # FigureManager base class.
        draw_if_interactive()

        if _INSTALL_FIG_OBSERVER:
            fig.stale_callback = _auto_draw_if_interactive

    if clear:
        manager.canvas.figure.clear()

    return manager.canvas.figure


def _auto_draw_if_interactive(fig, val):
    """
    An internal helper function for making sure that auto-redrawing
    works as intended in the plain python repl.

    Parameters
    ----------
    fig : Figure
        A figure object which is assumed to be associated with a canvas
    """
    if (val and matplotlib.is_interactive()
            and not fig.canvas.is_saving()
            and not fig.canvas._is_idle_drawing):
        # Some artists can mark themselves as stale in the middle of drawing
        # (e.g. axes position & tick labels being computed at draw time), but
        # this shouldn't trigger a redraw because the current redraw will
        # already take them into account.
        with fig.canvas._idle_draw_cntx():
            fig.canvas.draw_idle()


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

    If no current figure exists, a new one is created using
    `~.pyplot.figure()`.
    """
    manager = _pylab_helpers.Gcf.get_active()
    if manager is not None:
        return manager.canvas.figure
    else:
        return figure()


def fignum_exists(num):
    """Return whether the figure with the given id exists."""
    return _pylab_helpers.Gcf.has_fignum(num) or num in get_figlabels()


def get_fignums():
    """Return a list of existing figure numbers."""
    return sorted(_pylab_helpers.Gcf.figs)


def get_figlabels():
    """Return a list of existing figure labels."""
    managers = _pylab_helpers.Gcf.get_all_fig_managers()
    managers.sort(key=lambda m: m.num)
    return [m.canvas.figure.get_label() for m in managers]


def get_current_fig_manager():
    """
    Return the figure manager of the current figure.

    The figure manager is a container for the actual backend-depended window
    that displays the figure on screen.

    If no current figure exists, a new one is created, and its figure
    manager is returned.

    Returns
    -------
    `.FigureManagerBase` or backend-dependent subclass thereof
    """
    return gcf().canvas.manager


@_copy_docstring_and_deprecators(FigureCanvasBase.mpl_connect)
def connect(s, func):
    return gcf().canvas.mpl_connect(s, func)


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


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

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

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

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


def clf():
    """Clear the current figure."""
    gcf().clf()


def draw():
    """
    Redraw the current figure.

    This is used to update a figure that has been altered, but not
    automatically re-drawn.  If interactive mode is on (via `.ion()`), this
    should be only rarely needed, but there may be ways to modify the state of
    a figure without marking it as "stale".  Please report these cases as bugs.

    This is equivalent to calling ``fig.canvas.draw_idle()``, where ``fig`` is
    the current figure.
    """
    gcf().canvas.draw_idle()


@_copy_docstring_and_deprecators(Figure.savefig)
def savefig(*args, **kwargs):
    fig = gcf()
    res = fig.savefig(*args, **kwargs)
    fig.canvas.draw_idle()   # need this if 'transparent=True' to reset colors
    return res


## Putting things in figures ##


def figlegend(*args, **kwargs):
    return gcf().legend(*args, **kwargs)
if Figure.legend.__doc__:
    figlegend.__doc__ = Figure.legend.__doc__.replace("legend(", "figlegend(")


## Axes ##

@docstring.dedent_interpd
def axes(arg=None, **kwargs):
    """
    Add an axes to the current figure and make it the current axes.

    Call signatures::

        plt.axes()
        plt.axes(rect, projection=None, polar=False, **kwargs)
        plt.axes(ax)

    Parameters
    ----------
    arg : None or 4-tuple
        The exact behavior of this function depends on the type:

        - *None*: A new full window axes is added using
          ``subplot(**kwargs)``.
        - 4-tuple of floats *rect* = ``[left, bottom, width, height]``.
          A new axes is added with dimensions *rect* in normalized
          (0, 1) units using `~.Figure.add_axes` on the current figure.

    projection : {None, 'aitoff', 'hammer', 'lambert', 'mollweide', \
'polar', 'rectilinear', str}, optional
        The projection type of the `~.axes.Axes`. *str* is the name of
        a custom projection, see `~matplotlib.projections`. The default
        None results in a 'rectilinear' projection.

    polar : bool, default: False
        If True, equivalent to projection='polar'.

    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.

    Returns
    -------
    `~.axes.Axes`, or a subclass of `~.axes.Axes`
        The returned axes class depends on the projection used. It is
        `~.axes.Axes` if rectilinear projection is used and
        `.projections.polar.PolarAxes` if polar projection is used.

    Other Parameters
    ----------------
    **kwargs
        This method also takes the keyword arguments for
        the returned axes class. The keyword arguments for the
        rectilinear axes class `~.axes.Axes` can be found in
        the following table but there might also be other keyword
        arguments if another projection is used, see the actual axes
        class.

        %(Axes_kwdoc)s

    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')
    """
    fig = gcf()
    if arg is None:
        return fig.add_subplot(**kwargs)
    else:
        return fig.add_axes(arg, **kwargs)


def delaxes(ax=None):
    """
    Remove an `~.axes.Axes` (defaulting to the current axes) from its figure.
    """
    if ax is None:
        ax = gca()
    ax.remove()


def sca(ax):
    """
    Set the current Axes to *ax* and the current Figure to the parent of *ax*.
    """
    figure(ax.figure)
    ax.figure.sca(ax)


def cla():
    """Clear the current axes."""
    # Not generated via boilerplate.py to allow a different docstring.
    return gca().cla()


## More ways of creating axes ##

@docstring.dedent_interpd
def subplot(*args, **kwargs):
    """
    Add an Axes to the current figure or retrieve an existing Axes.

    This is a wrapper of `.Figure.add_subplot` which provides additional
    behavior when working with the implicit API (see the notes section).

    Call signatures::

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

    Parameters
    ----------
    *args : int, (int, int, *index*), or `.SubplotSpec`, default: (1, 1, 1)
        The position of the subplot described by one of

        - Three integers (*nrows*, *ncols*, *index*). The subplot will take the
          *index* position on a grid with *nrows* rows and *ncols* columns.
          *index* starts at 1 in the upper left corner and increases to the
          right. *index* can also be a two-tuple specifying the (*first*,
          *last*) indices (1-based, and including *last*) of the subplot, e.g.,
          ``fig.add_subplot(3, 1, (1, 2))`` makes a subplot that spans the
          upper 2/3 of the figure.
        - A 3-digit integer. The digits are interpreted as if given separately
          as three single-digit integers, i.e. ``fig.add_subplot(235)`` is the
          same as ``fig.add_subplot(2, 3, 5)``. Note that this can only be used
          if there are no more than 9 subplots.
        - A `.SubplotSpec`.

    projection : {None, 'aitoff', 'hammer', 'lambert', 'mollweide', \
'polar', 'rectilinear', str}, optional
        The projection type of the subplot (`~.axes.Axes`). *str* is the name
        of a custom projection, see `~matplotlib.projections`. The default
        None results in a 'rectilinear' projection.

    polar : bool, default: False
        If True, equivalent to projection='polar'.

    sharex, sharey : `~.axes.Axes`, optional
        Share the x or y `~matplotlib.axis` with sharex and/or sharey. The
        axis will have the same limits, ticks, and scale as the axis of the
        shared axes.

    label : str
        A label for the returned axes.

    Returns
    -------
    `.axes.SubplotBase`, or another 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
        is used and `.projections.polar.PolarAxes` if polar projection
        is used. The returned axes is then a subplot subclass of the
        base class.

    Other Parameters
    ----------------
    **kwargs
        This method also takes the keyword arguments for the returned axes
        base class; except for the *figure* argument. The keyword arguments
        for the rectilinear base class `~.axes.Axes` can be found in
        the following table but there might also be other keyword
        arguments if another projection is used.

        %(Axes_kwdoc)s

    Notes
    -----
    Creating a new Axes will delete any pre-existing Axes that
    overlaps with it beyond sharing a boundary::

        import matplotlib.pyplot as plt
        # plot a line, implicitly creating a subplot(111)
        plt.plot([1, 2, 3])
        # now create a subplot which represents the top plot of a grid
        # with 2 rows and 1 column. Since this subplot will overlap the
        # first, the plot (and its axes) previously created, will be removed
        plt.subplot(211)

    If you do not want this behavior, use the `.Figure.add_subplot` method
    or the `.pyplot.axes` function instead.

    If no *kwargs* are passed and there exists an Axes in the location
    specified by *args* then that Axes will be returned rather than a new
    Axes being created.

    If *kwargs* are passed and there exists an Axes in the location
    specified by *args*, the projection type is the same, and the
    *kwargs* match with the existing Axes, then the existing Axes is
    returned.  Otherwise a new Axes is created with the specified
    parameters.  We save a reference to the *kwargs* which we use
    for this comparison.  If any of the values in *kwargs* are
    mutable we will not detect the case where they are mutated.
    In these cases we suggest using `.Figure.add_subplot` and the
    explicit Axes API rather than the implicit pyplot API.

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

    Examples
    --------
    ::

        plt.subplot(221)

        # equivalent but more general
        ax1 = plt.subplot(2, 2, 1)

        # add a subplot with no frame
        ax2 = plt.subplot(222, frameon=False)

        # add a polar subplot
        plt.subplot(223, projection='polar')

        # add a red subplot that shares the x-axis with ax1
        plt.subplot(224, sharex=ax1, facecolor='red')

        # delete ax2 from the figure
        plt.delaxes(ax2)

        # add ax2 to the figure again
        plt.subplot(ax2)

        # make the first axes "current" again
        plt.subplot(221)

    """
    # Here we will only normalize `polar=True` vs `projection='polar'` and let
    # downstream code deal with the rest.
    unset = object()
    projection = kwargs.get('projection', unset)
    polar = kwargs.pop('polar', unset)
    if polar is not unset and polar:
        # if we got mixed messages from the user, raise
        if projection is not unset and projection != 'polar':
            raise ValueError(
                f"polar={polar}, yet projection={projection!r}. "
                "Only one of these arguments should be supplied."
            )
        kwargs['projection'] = projection = 'polar'

    # 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):
        _api.warn_external("The subplot index argument to subplot() appears "
                           "to be a boolean. Did you intend to use "
                           "subplots()?")
    # Check for nrows and ncols, which are not valid subplot args:
    if 'nrows' in kwargs or 'ncols' in kwargs:
        raise TypeError("subplot() got an unexpected keyword argument 'ncols' "
                        "and/or 'nrows'.  Did you intend to call subplots()?")

    fig = gcf()

    # First, search for an existing subplot with a matching spec.
    key = SubplotSpec._from_subplot_args(fig, args)

    for ax in fig.axes:
        # if we found an axes at the position sort out if we can re-use it
        if hasattr(ax, 'get_subplotspec') and ax.get_subplotspec() == key:
            # if the user passed no kwargs, re-use
            if kwargs == {}:
                break
            # if the axes class and kwargs are identical, reuse
            elif ax._projection_init == fig._process_projection_requirements(
                *args, **kwargs
            ):
                break
    else:
        # we have exhausted the known Axes and none match, make a new one!
        ax = fig.add_subplot(*args, **kwargs)

    fig.sca(ax)

    bbox = ax.bbox
    axes_to_delete = []
    for other_ax in fig.axes:
        if other_ax == ax:
            continue
        if bbox.fully_overlaps(other_ax.bbox):
            axes_to_delete.append(other_ax)
    for ax_to_del in axes_to_delete:
        delaxes(ax_to_del)

    return ax


@_api.make_keyword_only("3.3", "sharex")
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, default: 1
        Number of rows/columns of the subplot grid.

    sharex, sharey : bool or {'none', 'all', 'row', 'col'}, default: False
        Controls sharing of properties among x (*sharex*) or y (*sharey*)
        axes:

        - True or 'all': x- or y-axis will be shared among all subplots.
        - False or 'none': each subplot x- or y-axis will be independent.
        - 'row': each subplot row will share an x- or y-axis.
        - 'col': each subplot column will share an x- or y-axis.

        When subplots have a shared x-axis along a column, only the x tick
        labels of the bottom subplot are created. Similarly, when subplots
        have a shared y-axis along a row, only the y tick labels of the first
        column subplot are created. To later turn other subplots' ticklabels
        on, use `~matplotlib.axes.Axes.tick_params`.

        When subplots have a shared axis that has units, calling
        `~matplotlib.axis.Axis.set_units` will update each axis with the
        new units.

    squeeze : bool, default: True
        - If True, extra dimensions are squeezed out from the returned
          array of `~matplotlib.axes.Axes`:

          - if only one subplot is constructed (nrows=ncols=1), the
            resulting single Axes object is returned as a scalar.
          - for Nx1 or 1xM subplots, the returned object is a 1D numpy
            object array of Axes objects.
          - for NxM, subplots with N>1 and M>1 are returned as a 2D array.

        - If False, no squeezing at all is done: the returned Axes object is
          always a 2D array containing Axes instances, even if it ends up
          being 1x1.

    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` or array of Axes
        *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.subplots(1, 2)
            fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)

        The names ``ax`` and pluralized ``axs`` are preferred over ``axes``
        because for the latter it's not clear if it refers to a single
        `~.axes.Axes` instance or a collection of these.

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

    Examples
    --------
    ::

        # First create some toy data:
        x = np.linspace(0, 2*np.pi, 400)
        y = np.sin(x**2)

        # Create just a figure and only one subplot
        fig, ax = plt.subplots()
        ax.plot(x, y)
        ax.set_title('Simple plot')

        # Create two subplots and unpack the output array immediately
        f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
        ax1.plot(x, y)
        ax1.set_title('Sharing Y axis')
        ax2.scatter(x, y)

        # Create four polar axes and access them through the returned array
        fig, axs = plt.subplots(2, 2, subplot_kw=dict(projection="polar"))
        axs[0, 0].plot(x, y)
        axs[1, 1].scatter(x, y)

        # Share a X axis with each column of subplots
        plt.subplots(2, 2, sharex='col')

        # Share a Y axis with each row of subplots
        plt.subplots(2, 2, sharey='row')

        # Share both X and Y axes with all subplots
        plt.subplots(2, 2, sharex='all', sharey='all')

        # Note that this is the same as
        plt.subplots(2, 2, sharex=True, sharey=True)

        # Create figure number 10 with a single subplot
        # and clears it if it already exists.
        fig, ax = plt.subplots(num=10, clear=True)

    """
    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 subplot_mosaic(mosaic, *, subplot_kw=None, gridspec_kw=None,
                   empty_sentinel='.', **fig_kw):
    """
    Build a layout of Axes based on ASCII art or nested lists.

    This is a helper function to build complex GridSpec layouts visually.

    .. note ::

       This API is provisional and may be revised in the future based on
       early user feedback.


    Parameters
    ----------
    mosaic : list of list of {hashable or nested} or str

        A visual layout of how you want your Axes to be arranged
        labeled as strings.  For example ::

           x = [['A panel', 'A panel', 'edge'],
                ['C panel', '.',       'edge']]

        Produces 4 axes:

        - 'A panel' which is 1 row high and spans the first two columns
        - 'edge' which is 2 rows high and is on the right edge
        - 'C panel' which in 1 row and 1 column wide in the bottom left
        - a blank space 1 row and 1 column wide in the bottom center

        Any of the entries in the layout can be a list of lists
        of the same form to create nested layouts.

        If input is a str, then it must be of the form ::

          '''
          AAE
          C.E
          '''

        where each character is a column and each line is a row.
        This only allows only single character Axes labels and does
        not allow nesting but is very terse.

    subplot_kw : dict, optional
        Dictionary with keywords passed to the `.Figure.add_subplot` call
        used to create each subplot.

    gridspec_kw : dict, optional
        Dictionary with keywords passed to the `.GridSpec` constructor used
        to create the grid the subplots are placed on.

    empty_sentinel : object, optional
        Entry in the layout to mean "leave this space empty".  Defaults
        to ``'.'``. Note, if *layout* is a string, it is processed via
        `inspect.cleandoc` to remove leading white space, which may
        interfere with using white-space as the empty sentinel.

    **fig_kw
        All additional keyword arguments are passed to the
        `.pyplot.figure` call.

    Returns
    -------
    fig : `~.figure.Figure`
       The new figure

    dict[label, Axes]
       A dictionary mapping the labels to the Axes objects.  The order of
       the axes is left-to-right and top-to-bottom of their position in the
       total layout.

    """
    fig = figure(**fig_kw)
    ax_dict = fig.subplot_mosaic(
        mosaic,
        subplot_kw=subplot_kw,
        gridspec_kw=gridspec_kw,
        empty_sentinel=empty_sentinel
    )
    return fig, ax_dict


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

    Parameters
    ----------
    shape : (int, int)
        Number of rows and of columns of the grid in which to place axis.
    loc : (int, int)
        Row number and column number of the axis location within the grid.
    rowspan : int, default: 1
        Number of rows for the axis to span downwards.
    colspan : int, default: 1
        Number of columns for the axis to span to the right.
    fig : `.Figure`, optional
        Figure to place the subplot in. Defaults to the current figure.
    **kwargs
        Additional keyword arguments are handed to `~.Figure.add_subplot`.

    Returns
    -------
    `.axes.SubplotBase`, or another 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 is used
        and `.projections.polar.PolarAxes` if polar projection is used.  The
        returned axes is then a subplot subclass of the base class.

    Notes
    -----
    The following call ::

        ax = subplot2grid((nrows, ncols), (row, col), rowspan, colspan)

    is identical to ::

        fig = gcf()
        gs = fig.add_gridspec(nrows, ncols)
        ax = fig.add_subplot(gs[row:row+rowspan, col:col+colspan])
    """

    if fig is None:
        fig = gcf()

    rows, cols = shape
    gs = GridSpec._check_gridspec_exists(fig, rows, cols)

    subplotspec = gs.new_subplotspec(loc, rowspan=rowspan, colspan=colspan)
    ax = fig.add_subplot(subplotspec, **kwargs)
    bbox = ax.bbox
    axes_to_delete = []
    for other_ax in fig.axes:
        if other_ax == ax:
            continue
        if bbox.fully_overlaps(other_ax.bbox):
            axes_to_delete.append(other_ax)
    for ax_to_del in axes_to_delete:
        delaxes(ax_to_del)

    return ax


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

    A `matplotlib.widgets.SubplotTool` instance is returned. You must maintain
    a reference to the instance to keep the associated callbacks alive.
    """
    if targetfig is None:
        targetfig = gcf()
    with rc_context({"toolbar": "none"}):  # No navbar for the toolfig.
        # Use new_figure_manager() instead of figure() so that the figure
        # doesn't get registered with pyplot.
        manager = new_figure_manager(-1, (6, 3))
    manager.set_window_title("Subplot configuration tool")
    tool_fig = manager.canvas.figure
    tool_fig.subplots_adjust(top=0.9)
    manager.show()
    return SubplotTool(targetfig, tool_fig)


# After deprecation elapses, this can be autogenerated by boilerplate.py.
@_api.make_keyword_only("3.3", "pad")
def tight_layout(pad=1.08, h_pad=None, w_pad=None, rect=None):
    """
    Adjust the padding between and around subplots.

    Parameters
    ----------
    pad : float, default: 1.08
        Padding between the figure edge and the edges of subplots,
        as a fraction of the font size.
    h_pad, w_pad : float, default: *pad*
        Padding (height/width) between edges of adjacent subplots,
        as a fraction of the font size.
    rect : tuple (left, bottom, right, top), default: (0, 0, 1, 1)
        A rectangle in normalized figure coordinates into which the whole
        subplots area (including labels) will fit.
    """
    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:
        locs = ax.get_xticks()
        if labels is not None:
            raise TypeError("xticks(): Parameter 'labels' can't be set "
                            "without setting 'ticks'")
    else:
        locs = ax.set_xticks(ticks)

    if labels is None:
        labels = ax.get_xticklabels()
    else:
        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 ytick locations.  Passing an empty list removes all yticks.
    labels : array-like, optional
        The labels to place at the given *ticks* locations.  This argument can
        only be passed if *ticks* is passed as well.
    **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:
        locs = ax.get_yticks()
        if labels is not None:
            raise TypeError("yticks(): Parameter 'labels' can't be set "
                            "without setting 'ticks'")
    else:
        locs = ax.set_yticks(ticks)

    if labels is None:
        labels = ax.get_yticklabels()
    else:
        labels = ax.set_yticklabels(labels, **kwargs)
    for l in labels:
        l.update(kwargs)

    return locs, labels


def rgrids(radii=None, labels=None, angle=None, fmt=None, **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 : list of `.lines.Line2D`
        The radial gridlines.

    labels : list of `.text.Text`
        The tick labels.

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

    See Also
    --------
    .pyplot.thetagrids
    .projections.polar.PolarAxes.set_rgrids
    .Axis.get_gridlines
    .Axis.get_ticklabels

    Examples
    --------
    ::

      # set the locations of the radial gridlines
      lines, labels = rgrids( (0.25, 0.5, 1.0) )

      # set the locations and labels of the radial gridlines
      lines, labels = rgrids( (0.25, 0.5, 1.0), ('Tom', 'Dick', 'Harry' ))
    """
    ax = gca()
    if not isinstance(ax, PolarAxes):
        raise RuntimeError('rgrids only defined for polar axes')
    if all(p is None for p in [radii, labels, angle, fmt]) and not kwargs:
        lines = ax.yaxis.get_gridlines()
        labels = ax.yaxis.get_ticklabels()
    else:
        lines, labels = ax.set_rgrids(
            radii, labels=labels, angle=angle, fmt=fmt, **kwargs)
    return lines, labels


def thetagrids(angles=None, labels=None, fmt=None, **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 : list of `.lines.Line2D`
        The theta gridlines.

    labels : list of `.text.Text`
        The tick labels.

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

    See Also
    --------
    .pyplot.rgrids
    .projections.polar.PolarAxes.set_thetagrids
    .Axis.get_gridlines
    .Axis.get_ticklabels

    Examples
    --------
    ::

      # set the locations of the angular gridlines
      lines, labels = thetagrids(range(45, 360, 90))

      # set the locations and labels of the angular gridlines
      lines, labels = thetagrids(range(45, 360, 90), ('NE', 'NW', 'SW', 'SE'))
    """
    ax = gca()
    if not isinstance(ax, PolarAxes):
        raise RuntimeError('thetagrids only defined for polar axes')
    if all(param is None for param in [angles, labels, fmt]) and not kwargs:
        lines = ax.xaxis.get_ticklines()
        labels = ax.xaxis.get_ticklabels()
    else:
        lines, labels = ax.set_thetagrids(angles,
                                          labels=labels, fmt=fmt, **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 colormaps which are
    the recommended colormaps 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
    <https://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, `.ListedColormap` is used,
    not `.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 colormap 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 colormaps:

      ================  =================================================
      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
      turbo         Spectral map (purple-blue-green-yellow-orange-red) with
                    a bright center and darker endpoints. A smoother
                    alternative to jet.
      ============= =======================================================

    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
      <https://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_registry)


def _setup_pyplot_info_docstrings():
    """
    Setup the docstring of `plotting` and of the colormap-setting functions.

    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)

    for cm_name in colormaps():
        if cm_name in globals():
            globals()[cm_name].__doc__ = f"""
    Set the colormap to {cm_name!r}.

    This changes the default colormap as well as the colormap of the current
    image if there is one. See ``help(colormaps)`` for more information.
    """


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


@_copy_docstring_and_deprecators(Figure.colorbar)
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).')
    ret = gcf().colorbar(mappable, cax=cax, ax=ax, **kw)
    return ret


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 : `~matplotlib.colors.Colormap` or str
        A colormap instance or the name of a registered colormap.

    See Also
    --------
    colormaps
    matplotlib.cm.register_cmap
    matplotlib.cm.get_cmap
    """
    cmap = cm.get_cmap(cmap)

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

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


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


@_copy_docstring_and_deprecators(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 : 2D array-like
        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
    -------
    `~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():
        ax = gca()
        if isinstance(ax, PolarAxes):
            return ax
        else:
            _api.warn_external('Trying to create polar plot on an Axes '
                               'that does not have a polar projection.')
    ax = axes(projection="polar")
    ret = ax.plot(*args, **kwargs)
    return ret


# 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 cbook._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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(Figure.text)
def figtext(x, y, s, fontdict=None, **kwargs):
    return gcf().text(x, y, s, fontdict=fontdict, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@_copy_docstring_and_deprecators(Figure.gca)
def gca(**kwargs):
    return gcf().gca(**kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@_copy_docstring_and_deprecators(Figure._gci)
def gci():
    return gcf()._gci()


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@_copy_docstring_and_deprecators(Figure.ginput)
def ginput(
        n=1, timeout=30, show_clicks=True,
        mouse_add=MouseButton.LEFT, mouse_pop=MouseButton.RIGHT,
        mouse_stop=MouseButton.MIDDLE):
    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.
@_copy_docstring_and_deprecators(Figure.subplots_adjust)
def subplots_adjust(
        left=None, bottom=None, right=None, top=None, wspace=None,
        hspace=None):
    return gcf().subplots_adjust(
        left=left, bottom=bottom, right=right, top=top, wspace=wspace,
        hspace=hspace)


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


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


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


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(Axes.axline)
def axline(xy1, xy2=None, *, slope=None, **kwargs):
    return gca().axline(xy1, xy2=xy2, slope=slope, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(Axes.bar_label)
def bar_label(
        container, labels=None, *, fmt='%g', label_type='edge',
        padding=0, **kwargs):
    return gca().bar_label(
        container, labels=labels, fmt=fmt, label_type=label_type,
        padding=padding, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(Axes.clabel)
def clabel(CS, levels=None, **kwargs):
    return gca().clabel(CS, levels=levels, **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(Axes.stairs)
def stairs(
        values, edges=None, *, orientation='vertical', baseline=0,
        fill=False, data=None, **kwargs):
    return gca().stairs(
        values, edges=edges, orientation=orientation,
        baseline=baseline, fill=fill,
        **({"data": data} if data is not None else {}), **kwargs)


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(Axes.hlines)
def hlines(
        y, xmin, xmax, colors=None, 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.
@_copy_docstring_and_deprecators(Axes.imshow)
def imshow(
        X, cmap=None, norm=None, aspect=None, interpolation=None,
        alpha=None, vmin=None, vmax=None, origin=None, extent=None, *,
        filternorm=True, filterrad=4.0, 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,
        filternorm=filternorm, filterrad=filterrad, 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.
@_copy_docstring_and_deprecators(Axes.legend)
def legend(*args, **kwargs):
    return gca().legend(*args, **kwargs)


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


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


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


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@_copy_docstring_and_deprecators(Axes.pcolor)
def pcolor(
        *args, shading=None, alpha=None, norm=None, cmap=None,
        vmin=None, vmax=None, data=None, **kwargs):
    __ret = gca().pcolor(
        *args, shading=shading, 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.
@_copy_docstring_and_deprecators(Axes.pcolormesh)
def pcolormesh(
        *args, alpha=None, norm=None, cmap=None, vmin=None,
        vmax=None, shading=None, 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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(Axes.pie)
def pie(
        x, explode=None, labels=None, colors=None, autopct=None,
        pctdistance=0.6, shadow=False, labeldistance=1.1,
        startangle=0, radius=1, counterclock=True, wedgeprops=None,
        textprops=None, center=(0, 0), frame=False,
        rotatelabels=False, *, normalize=None, 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, normalize=normalize,
        **({"data": data} if data is not None else {}))


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(Axes.scatter)
def scatter(
        x, y, s=None, c=None, marker=None, cmap=None, norm=None,
        vmin=None, vmax=None, alpha=None, linewidths=None, *,
        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,
        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.
@_copy_docstring_and_deprecators(Axes.semilogx)
def semilogx(*args, **kwargs):
    return gca().semilogx(*args, **kwargs)


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


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


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


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


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(Axes.vlines)
def vlines(
        x, ymin, ymax, colors=None, 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.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(Axes._sci)
def sci(im):
    return gca()._sci(im)


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


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


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


# Autogenerated by boilerplate.py.  Do not edit as changes will be lost.
@_copy_docstring_and_deprecators(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.
@_copy_docstring_and_deprecators(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_cmap('autumn')
def bone(): set_cmap('bone')
def cool(): set_cmap('cool')
def copper(): set_cmap('copper')
def flag(): set_cmap('flag')
def gray(): set_cmap('gray')
def hot(): set_cmap('hot')
def hsv(): set_cmap('hsv')
def jet(): set_cmap('jet')
def pink(): set_cmap('pink')
def prism(): set_cmap('prism')
def spring(): set_cmap('spring')
def summer(): set_cmap('summer')
def winter(): set_cmap('winter')
def magma(): set_cmap('magma')
def inferno(): set_cmap('inferno')
def plasma(): set_cmap('plasma')
def viridis(): set_cmap('viridis')
def nipy_spectral(): set_cmap('nipy_spectral')


_setup_pyplot_info_docstrings()
