text: # 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.
  """
  from __future__ import (absolute_import, division, print_function,
                          unicode_literals)
  
  import six
  
  import sys
  import warnings
  import time
  import types
  from cycler import cycler
  import matplotlib
  import matplotlib.colorbar
  from matplotlib import style
  from matplotlib import _pylab_helpers, interactive
  from matplotlib.cbook import dedent, silent_list, is_numlike
  from matplotlib.cbook import _string_to_bool
  from matplotlib.cbook import deprecated
  from matplotlib import docstring
  from matplotlib.backend_bases import FigureCanvasBase
  from matplotlib.figure import Figure, figaspect
  from matplotlib.gridspec import GridSpec
  from matplotlib.image import imread as _imread
  from matplotlib.image import imsave as _imsave
  from matplotlib import rcParams, rcParamsDefault, get_backend
  from matplotlib import rc_context
  from matplotlib.rcsetup import interactive_bk as _interactive_bk
  from matplotlib.artist import getp, get, Artist
  from matplotlib.artist import setp as _setp
  from matplotlib.axes import Axes, Subplot
  from matplotlib.projections import PolarAxes
  from matplotlib import mlab  # for csv2rec, detrend_none, window_hanning
  from matplotlib.scale import get_scale_docs, get_scale_names
  
  from matplotlib import cm
  from matplotlib.cm import get_cmap, register_cmap
  
  import numpy as np
  
  # We may not need the following imports here:
  from matplotlib.colors import Normalize
  from matplotlib.lines import Line2D
  from matplotlib.text import Text, Annotation
  from matplotlib.patches import Polygon, Rectangle, Circle, Arrow
  from matplotlib.widgets import SubplotTool, Button, Slider, Widget
  
  from .ticker import TickHelper, Formatter, FixedFormatter, NullFormatter,\
             FuncFormatter, FormatStrFormatter, ScalarFormatter,\
             LogFormatter, LogFormatterExponent, LogFormatterMathtext,\
             Locator, IndexLocator, FixedLocator, NullLocator,\
             LinearLocator, LogLocator, AutoLocator, MultipleLocator,\
             MaxNLocator
  
  from matplotlib.backends import pylab_setup
  
  from matplotlib.font_manager import FontProperties, rcParams
  rcParams['axes.unicode_minus'] = False
  rcParams['savefig.dpi'] = 600 # 图片像素
  rcParams['figure.dpi'] = 600 # 分辨率
  
  ## Backend detection ##
  def _backend_selection():
      """ If rcParams['backend_fallback'] is true, check to see if the
          current backend is compatible with the current running event
          loop, and if not switches to a compatible one.
      """
      backend = rcParams['backend']
      if not rcParams['backend_fallback'] or backend not in _interactive_bk:
          return
      is_agg_backend = rcParams['backend'].endswith('Agg')
      if 'wx' in sys.modules and not backend in ('WX', 'WXAgg'):
          import wx
          if wx.App.IsMainLoopRunning():
              rcParams['backend'] = 'wx' + 'Agg' * is_agg_backend
      elif 'PyQt4.QtCore' in sys.modules and not backend == 'Qt4Agg':
          import PyQt4.QtGui
          if not PyQt4.QtGui.qApp.startingUp():
              # The mainloop is running.
              rcParams['backend'] = 'qt4Agg'
      elif 'PyQt5.QtCore' in sys.modules and not backend == 'Qt5Agg':
          import PyQt5.QtWidgets
          if not PyQt5.QtWidgets.qApp.startingUp():
              # The mainloop is running.
              rcParams['backend'] = 'qt5Agg'
      elif ('gtk' in sys.modules and
            backend not in ('GTK', 'GTKAgg', 'GTKCairo')):
          if 'gi' in sys.modules:
              from gi.repository import GObject
              ml = GObject.MainLoop
          else:
              import gobject
              ml = gobject.MainLoop
          if ml().is_running():
              rcParams['backend'] = 'gtk' + 'Agg' * is_agg_backend
      elif 'Tkinter' in sys.modules and not backend == 'TkAgg':
          # import Tkinter
          pass  # what if anything do we need to do for tkinter?
  
  _backend_selection()
  
  ## Global ##
  
  _backend_mod, new_figure_manager, draw_if_interactive, _show = pylab_setup()
  
  _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 with IPython terminals and kernels,
      as well as 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():
      """
      Uninstalls the matplotlib display hook.
  
      .. warning
  
         Need IPython >= 2 for this to work.  For IPython < 2 will raise a
         ``NotImplementedError``
  
      .. warning
  
         If you are using vanilla python and have installed another
         display hook this will reset ``sys.displayhook`` to what ever
         function was there when matplotlib installed it's displayhook,
         possibly discarding your changes.
      """
      global _IP_REGISTERED
      global _INSTALL_FIG_OBSERVER
      if _IP_REGISTERED:
          from IPython import get_ipython
          ip = get_ipython()
          try:
              ip.events.unregister('post_execute', _IP_REGISTERED)
          except AttributeError:
              raise NotImplementedError("Can not unregister events "
                                        "in IPython < 2.0")
          _IP_REGISTERED = None
  
      if _INSTALL_FIG_OBSERVER:
          _INSTALL_FIG_OBSERVER = False
  
  
  draw_all = _pylab_helpers.Gcf.draw_all
  
  
  @docstring.copy_dedent(Artist.findobj)
  def findobj(o=None, match=None, include_self=True):
      if o is None:
          o = gcf()
      return o.findobj(match, include_self=include_self)
  
  
  def switch_backend(newbackend):
      """
      Switch the default backend.  This feature is **experimental**, and
      is only expected to work switching to an image backend.  e.g., if
      you have a bunch of PostScript scripts that you want to run from
      an interactive ipython session, you may want to switch to the PS
      backend before running them to avoid having a bunch of GUI windows
      popup.  If you try to interactively switch from one GUI backend to
      another, you will explode.
  
      Calling this command will close all open windows.
      """
      close('all')
      global _backend_mod, new_figure_manager, draw_if_interactive, _show
      matplotlib.use(newbackend, warn=False, force=True)
      from matplotlib.backends import pylab_setup
      _backend_mod, new_figure_manager, draw_if_interactive, _show = pylab_setup()
  
  
  def show(*args, **kw):
      """
      Display a figure.
      When running in ipython with its pylab mode, display all
      figures and return to the ipython prompt.
  
      In non-interactive mode, display all figures and block until
      the figures have been closed; in interactive mode it has no
      effect unless figures were created prior to a change from
      non-interactive to interactive mode (not recommended).  In
      that case it displays the figures but does not block.
  
      A single experimental keyword argument, *block*, may be
      set to True or False to override the blocking behavior
      described above.
      """
      global _show
      return _show(*args, **kw)
  
  
  def isinteractive():
      """
      Return status of interactive mode.
      """
      return matplotlib.is_interactive()
  
  
  def ioff():
      """Turn interactive mode off."""
      matplotlib.interactive(False)
      uninstall_repl_displayhook()
  
  
  def ion():
      """Turn interactive mode on."""
      matplotlib.interactive(True)
      install_repl_displayhook()
  
  
  def pause(interval):
      """
      Pause for *interval* seconds.
  
      If there is an active figure, it will be updated and displayed before the
      pause, and the GUI event loop (if any) will run during the pause.
  
      This can be used for crude animation.  For more complex animation, see
      :mod:`matplotlib.animation`.
  
      Note
      ----
      This function is experimental; its behavior may be changed or extended in a
      future release.
      """
      manager = _pylab_helpers.Gcf.get_active()
      if manager is not None:
          canvas = manager.canvas
          if canvas.figure.stale:
              canvas.draw_idle()
          show(block=False)
          canvas.start_event_loop(interval)
      else:
          time.sleep(interval)
  
  
  @docstring.copy_dedent(matplotlib.rc)
  def rc(*args, **kwargs):
      matplotlib.rc(*args, **kwargs)
  
  
  @docstring.copy_dedent(matplotlib.rc_context)
  def rc_context(rc=None, fname=None):
      return matplotlib.rc_context(rc, fname)
  
  
  @docstring.copy_dedent(matplotlib.rcdefaults)
  def rcdefaults():
      matplotlib.rcdefaults()
      if matplotlib.is_interactive():
          draw_all()
  
  
  # The current "image" (ScalarMappable) is retrieved or set
  # only via the pyplot interface using the following two
  # functions:
  def gci():
      """
      Get the current colorable artist.  Specifically, returns the
      current :class:`~matplotlib.cm.ScalarMappable` instance (image or
      patch collection), or *None* if no images or patch collections
      have been defined.  The commands :func:`~matplotlib.pyplot.imshow`
      and :func:`~matplotlib.pyplot.figimage` create
      :class:`~matplotlib.image.Image` instances, and the commands
      :func:`~matplotlib.pyplot.pcolor` and
      :func:`~matplotlib.pyplot.scatter` create
      :class:`~matplotlib.collections.Collection` instances.  The
      current image is an attribute of the current axes, or the nearest
      earlier axes in the current figure that contains an image.
      """
      return gcf()._gci()
  
  
  def sci(im):
      """
      Set the current image.  This image will be the target of colormap
      commands like :func:`~matplotlib.pyplot.jet`,
      :func:`~matplotlib.pyplot.hot` or
      :func:`~matplotlib.pyplot.clim`).  The current image is an
      attribute of the current axes.
      """
      gca()._sci(im)
  
  
  ## Any Artist ##
  # (getp is simply imported)
  @docstring.copy(_setp)
  def setp(*args, **kwargs):
      return _setp(*args, **kwargs)
  
  
  def xkcd(scale=1, length=100, randomness=2):
      """
      Turns on `xkcd <https://xkcd.com/>`_ sketch-style drawing mode.
      This will only have effect on things drawn after this function is
      called.
  
      For best results, the "Humor Sans" font should be installed: it is
      not included with matplotlib.
  
      Parameters
      ----------
      scale : float, optional
          The amplitude of the wiggle perpendicular to the source line.
      length : float, optional
          The length of the wiggle along the line.
      randomness : float, optional
          The scale factor by which the length is shrunken or expanded.
  
      Notes
      -----
      This function works by a number of rcParams, so it will probably
      override others you have set before.
  
      If you want the effects of this function to be temporary, it can
      be used as a context manager, for example::
  
          with plt.xkcd():
              # This figure will be in XKCD-style
              fig1 = plt.figure()
              # ...
  
          # This figure will be in regular style
          fig2 = plt.figure()
      """
      if rcParams['text.usetex']:
          raise RuntimeError(
              "xkcd mode is not compatible with text.usetex = True")
  
      from matplotlib import patheffects
      context = rc_context()
      try:
          rcParams['font.family'] = ['xkcd', 'Humor Sans', 'Comic Sans MS']
          rcParams['font.size'] = 14.0
          rcParams['path.sketch'] = (scale, length, randomness)
          rcParams['path.effects'] = [
              patheffects.withStroke(linewidth=4, foreground="w")]
          rcParams['axes.linewidth'] = 1.5
          rcParams['lines.linewidth'] = 2.0
          rcParams['figure.facecolor'] = 'white'
          rcParams['grid.linewidth'] = 0.0
          rcParams['axes.grid'] = False
          rcParams['axes.unicode_minus'] = False
          rcParams['axes.edgecolor'] = 'black'
          rcParams['xtick.major.size'] = 8
          rcParams['xtick.major.width'] = 3
          rcParams['ytick.major.size'] = 8
          rcParams['ytick.major.width'] = 3
      except:
          context.__exit__(*sys.exc_info())
          raise
      return context
  
  
  ## 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
             ):
      """
      Creates a new figure.
  
      Parameters
      ----------
  
      num : integer or string, optional, default: none
          If not provided, a new figure will be created, and the figure number
          will be incremented. The figure objects holds this number in a `number`
          attribute.
          If num is provided, and a figure with this id already exists, make
          it active, and returns a reference to it. If this figure does not
          exists, create it and returns it.
          If num is a string, the window title will be set to this figure's
          `num`.
  
      figsize : tuple of integers, optional, default: None
          width, height in inches. If not provided, defaults to rc
          figure.figsize.
  
      dpi : integer, optional, default: None
          resolution of the figure. If not provided, defaults to rc figure.dpi.
  
      facecolor :
          the background color. If not provided, defaults to rc figure.facecolor.
  
      edgecolor :
          the border color. If not provided, defaults to rc figure.edgecolor.
  
      frameon : bool, optional, default: True
          If False, suppress drawing the figure frame.
  
      FigureClass : class derived from matplotlib.figure.Figure
          Optionally use a custom Figure instance.
  
      clear : bool, optional, default: False
          If True and the figure already exists, then it is cleared.
  
      Returns
      -------
      figure : 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
          pylab interface. Additional kwargs will be passed to the figure init
          function.
  
      Notes
      -----
      If you are creating many figures, make sure you explicitly call "close"
      on the figures you are not using, because this will enable pylab
      to properly clean up the memory.
  
      rcParams defines the default values, which can be modified in the
      matplotlibrc file
  
      """
  
      if figsize is None:
          figsize = rcParams['figure.figsize']
      if dpi is None:
          dpi = rcParams['figure.dpi']
      if facecolor is None:
          facecolor = rcParams['figure.facecolor']
      if edgecolor is None:
          edgecolor = rcParams['figure.edgecolor']
  
      allnums = get_fignums()
      next_num = max(allnums) + 1 if allnums else 1
      figLabel = ''
      if num is None:
          num = next_num
      elif isinstance(num, six.string_types):
          figLabel = num
          allLabels = get_figlabels()
          if figLabel not in allLabels:
              if figLabel == 'all':
                  warnings.warn("close('all') closes all existing figures")
              num = next_num
          else:
              inum = allLabels.index(figLabel)
              num = allnums[inum]
      else:
          num = int(num)  # crude validation of num argument
  
      figManager = _pylab_helpers.Gcf.get_fig_manager(num)
      if figManager is None:
          max_open_warning = rcParams['figure.max_open_warning']
  
          if (max_open_warning >= 1 and len(allnums) >= max_open_warning):
              warnings.warn(
                  "More than %d figures have been opened. Figures "
                  "created through the pyplot interface "
                  "(`matplotlib.pyplot.figure`) are retained until "
                  "explicitly closed and may consume too much memory. "
                  "(To control this warning, see the rcParam "
                  "`figure.max_open_warning`)." %
                  max_open_warning, RuntimeWarning)
  
          if get_backend().lower() == 'ps':
              dpi = 72
  
          figManager = new_figure_manager(num, figsize=figsize,
                                          dpi=dpi,
                                          facecolor=facecolor,
                                          edgecolor=edgecolor,
                                          frameon=frameon,
                                          FigureClass=FigureClass,
                                          **kwargs)
  
          if figLabel:
              figManager.set_window_title(figLabel)
              figManager.canvas.figure.set_label(figLabel)
  
          # make this figure current on button press event
          def make_active(event):
              _pylab_helpers.Gcf.set_active(figManager)
  
          cid = figManager.canvas.mpl_connect('button_press_event', make_active)
          figManager._cidgcf = cid
  
          _pylab_helpers.Gcf.set_active(figManager)
          fig = figManager.canvas.figure
          fig.number = num
  
          # make sure backends (inline) that we don't ship that expect this
          # to be called in plotting commands to make the figure call show
          # still work.  There is probably a better way to do this in the
          # FigureManager base class.
          if matplotlib.is_interactive():
              draw_if_interactive()
  
          if _INSTALL_FIG_OBSERVER:
              fig.stale_callback = _auto_draw_if_interactive
  
      if clear:
          figManager.canvas.figure.clear()
  
      return figManager.canvas.figure
  
  
  def _auto_draw_if_interactive(fig, val):
      """
      This is an internal helper function for making sure that auto-redrawing
      works as intended in the plain python repl.
  
      Parameters
      ----------
      fig : Figure
          A figure object which is assumed to be associated with a canvas
      """
      if val and matplotlib.is_interactive() and not fig.canvas.is_saving():
          fig.canvas.draw_idle()
  
  
  def gcf():
      """Get a reference to the current figure."""
      figManager = _pylab_helpers.Gcf.get_active()
      if figManager is not None:
          return figManager.canvas.figure
      else:
          return figure()
  
  
  def fignum_exists(num):
      return _pylab_helpers.Gcf.has_fignum(num) or num in get_figlabels()
  
  
  def get_fignums():
      """Return a list of existing figure numbers."""
      return sorted(_pylab_helpers.Gcf.figs)
  
  
  def get_figlabels():
      """Return a list of existing figure labels."""
      figManagers = _pylab_helpers.Gcf.get_all_fig_managers()
      figManagers.sort(key=lambda m: m.num)
      return [m.canvas.figure.get_label() for m in figManagers]
  
  
  def get_current_fig_manager():
      figManager = _pylab_helpers.Gcf.get_active()
      if figManager is None:
          gcf()  # creates an active figure as a side effect
          figManager = _pylab_helpers.Gcf.get_active()
      return figManager
  
  
  @docstring.copy_dedent(FigureCanvasBase.mpl_connect)
  def connect(s, func):
      return get_current_fig_manager().canvas.mpl_connect(s, func)
  
  
  @docstring.copy_dedent(FigureCanvasBase.mpl_disconnect)
  def disconnect(cid):
      return get_current_fig_manager().canvas.mpl_disconnect(cid)
  
  
  def close(*args):
      """
      Close a figure window.
  
      ``close()`` by itself closes the current figure
  
      ``close(fig)`` closes the `~.Figure` instance *fig*
  
      ``close(num)`` closes the figure number *num*
  
      ``close(name)`` where *name* is a string, closes figure with that label
  
      ``close('all')`` closes all the figure windows
      """
  
      if len(args) == 0:
          figManager = _pylab_helpers.Gcf.get_active()
          if figManager is None:
              return
          else:
              _pylab_helpers.Gcf.destroy(figManager.num)
      elif len(args) == 1:
          arg = args[0]
          if arg == 'all':
              _pylab_helpers.Gcf.destroy_all()
          elif isinstance(arg, six.integer_types):
              _pylab_helpers.Gcf.destroy(arg)
          elif hasattr(arg, 'int'):
              # if we are dealing with a type UUID, we
              # can use its integer representation
              _pylab_helpers.Gcf.destroy(arg.int)
          elif isinstance(arg, six.string_types):
              allLabels = get_figlabels()
              if arg in allLabels:
                  num = get_fignums()[allLabels.index(arg)]
                  _pylab_helpers.Gcf.destroy(num)
          elif isinstance(arg, Figure):
              _pylab_helpers.Gcf.destroy_fig(arg)
          else:
              raise TypeError('Unrecognized argument type %s to close' % type(arg))
      else:
          raise TypeError('close takes 0 or 1 arguments')
  
  
  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 (:func:`.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.
  
      A more object-oriented alternative, given any
      :class:`~matplotlib.figure.Figure` instance, :attr:`fig`, that
      was created using a :mod:`~matplotlib.pyplot` function, is::
  
          fig.canvas.draw_idle()
      """
      get_current_fig_manager().canvas.draw_idle()
  
  
  @docstring.copy_dedent(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
  
  
  @docstring.copy_dedent(Figure.ginput)
  def ginput(*args, **kwargs):
      """
      Blocking call to interact with the figure.
  
      This will wait for *n* clicks from the user and return a list of the
      coordinates of each click.
  
      If *timeout* is negative, does not timeout.
      """
      return gcf().ginput(*args, **kwargs)
  
  
  @docstring.copy_dedent(Figure.waitforbuttonpress)
  def waitforbuttonpress(*args, **kwargs):
      """
      Blocking call to interact with the figure.
  
      This will wait for *n* key or mouse clicks from the user and
      return a list containing True's for keyboard clicks and False's
      for mouse clicks.
  
      If *timeout* is negative, does not timeout.
      """
      return gcf().waitforbuttonpress(*args, **kwargs)
  
  
  # Putting things in figures
  
  @docstring.copy_dedent(Figure.text)
  def figtext(*args, **kwargs):
      return gcf().text(*args, **kwargs, fontproperties = FontProperties(fname='/Library/Fonts/Songti.ttc'))
  
  
  @docstring.copy_dedent(Figure.suptitle)
  def suptitle(*args, **kwargs):
      return gcf().suptitle(*args, **kwargs, fontproperties = FontProperties(fname='/Library/Fonts/Songti.ttc'))
  
  
  @docstring.copy_dedent(Figure.figimage)
  def figimage(*args, **kwargs):
      return gcf().figimage(*args, **kwargs)
  
  
  def figlegend(*args, **kwargs):
      """
      Place a legend in the figure.
  
      *labels*
        a sequence of strings
  
      *handles*
        a sequence of :class:`~matplotlib.lines.Line2D` or
        :class:`~matplotlib.patches.Patch` instances
  
      *loc*
        can be a string or an integer specifying the legend
        location
  
      A :class:`matplotlib.legend.Legend` instance is returned.
  
      Examples
      --------
  
      To make a legend from existing artists on every axes::
  
        figlegend()
  
      To make a legend for a list of lines and labels::
  
        figlegend( (line1, line2, line3),
                   ('label1', 'label2', 'label3'),
                   'upper right' )
  
      .. seealso::
  
         :func:`~matplotlib.pyplot.legend`
  
      """
      return gcf().legend(*args, **kwargs, fontproperties = FontProperties(fname='/Library/Fonts/Songti.ttc'))
  
  
  ## Figure and Axes hybrid ##
  
  _hold_msg = """pyplot.hold is deprecated.
      Future behavior will be consistent with the long-time default:
      plot commands add elements without first clearing the
      Axes and/or Figure."""
  
  @deprecated("2.0", message=_hold_msg)
  def hold(b=None):
      """
      Set the hold state.  If *b* is None (default), toggle the
      hold state, else set the hold state to boolean value *b*::
  
        hold()      # toggle hold
        hold(True)  # hold is on
        hold(False) # hold is off
  
      When *hold* is *True*, subsequent plot commands will add elements to
      the current axes.  When *hold* is *False*, the current axes and
      figure will be cleared on the next plot command.
  
      """
  
      fig = gcf()
      ax = fig.gca()
  
      if b is not None:
          b = bool(b)
      fig._hold = b
      ax._hold = b
  
      # b=None toggles the hold state, so let's get get the current hold
      # state; but should pyplot hold toggle the rc setting - me thinks
      # not
      b = ax._hold
  
      # The comment above looks ancient; and probably the line below,
      # contrary to the comment, is equally ancient.  It will trigger
      # a second warning, but "Oh, well...".
      rc('axes', hold=b)
  
  @deprecated("2.0", message=_hold_msg)
  def ishold():
      """
      Return the hold status of the current axes.
      """
      return gca()._hold
  
  
  @deprecated("2.0", message=_hold_msg)
  def over(func, *args, **kwargs):
      """
      Call a function with hold(True).
  
      Calls::
  
        func(*args, **kwargs)
  
      with ``hold(True)`` and then restores the hold state.
  
      """
      ax = gca()
      h = ax._hold
      ax._hold = True
      func(*args, **kwargs)
      ax._hold = h
  
  ## Axes ##
  
  
  def axes(*args, **kwargs):
      """
      Add an axes to the figure.
  
      The axes is added at position *rect* specified by:
  
      - ``axes()`` by itself creates a default full ``subplot(111)`` window axis.
  
      - ``axes(rect, facecolor='w')`` where *rect* = [left, bottom, width,
        height] in normalized (0, 1) units.  *facecolor* is the background
        color for the axis, default white.
  
      - ``axes(h)`` where *h* is an axes instance makes *h* the current
        axis and the parent of *h* the current figure.
        An :class:`~matplotlib.axes.Axes` instance is returned.
  
      =========   ==============   ==============================================
      kwarg       Accepts          Description
      =========   ==============   ==============================================
      facecolor   color            the axes background color
      frameon     [True|False]     display the frame?
      sharex      otherax          current axes shares xaxis attribute
                                   with otherax
      sharey      otherax          current axes shares yaxis attribute
                                   with otherax
      polar       [True|False]     use a polar axes?
      aspect      [str | num]      ['equal', 'auto'] or a number.  If a number
                                   the ratio of y-unit/x-unit in screen-space.
                                   Also see
                                   :meth:`~matplotlib.axes.Axes.set_aspect`.
      =========   ==============   ==============================================
  
      Examples:
  
      * :file:`examples/pylab_examples/axes_demo.py` places custom axes.
      * :file:`examples/pylab_examples/shared_axis_demo.py` uses
        *sharex* and *sharey*.
  
      """
  
      nargs = len(args)
      if len(args) == 0:
          return subplot(111, **kwargs)
      if nargs > 1:
          raise TypeError('Only one non keyword arg to axes allowed')
      arg = args[0]
  
      if isinstance(arg, Axes):
          sca(arg)
          a = arg
      else:
          rect = arg
          a = gcf().add_axes(rect, **kwargs)
      return a
  
  
  def delaxes(*args):
      """
      Remove an axes from the current figure.  If *ax*
      doesn't exist, an error will be raised.
  
      ``delaxes()``: delete the current axes
      """
      if not len(args):
          ax = gca()
      else:
          ax = args[0]
      ret = gcf().delaxes(ax)
      return ret
  
  
  def sca(ax):
      """
      Set the current Axes instance to *ax*.
  
      The current Figure is updated to the parent of *ax*.
      """
      managers = _pylab_helpers.Gcf.get_all_fig_managers()
      for m in managers:
          if ax in m.canvas.figure.axes:
              _pylab_helpers.Gcf.set_active(m)
              m.canvas.figure.sca(ax)
              return
      raise ValueError("Axes instance argument was not found in a figure.")
  
  
  def gca(**kwargs):
      """
      Get the current :class:`~matplotlib.axes.Axes` instance on the
      current figure matching the given keyword args, or create one.
  
      Examples
      --------
      To get the current polar axes on the current figure::
  
          plt.gca(projection='polar')
  
      If the current axes doesn't exist, or isn't a polar one, the appropriate
      axes will be created and then returned.
  
      See Also
      --------
      matplotlib.figure.Figure.gca : The figure's gca method.
      """
      return gcf().gca(**kwargs)
  
  # More ways of creating axes:
  
  
  def subplot(*args, **kwargs):
      """
      Return a subplot axes at the given grid position.
  
      Call signature::
  
         subplot(nrows, ncols, index, **kwargs)
  
      In the current figure, create and return an `~.Axes`, at position *index*
      of a (virtual) grid of *nrows* by *ncols* axes.  Indexes go from 1 to
      ``nrows * ncols``, incrementing in row-major order.
  
      If *nrows*, *ncols* and *index* are all less than 10, they can also be
      given as a single, concatenated, three-digit number.
  
      For example, ``subplot(2, 3, 3)`` and ``subplot(233)`` both create an
      `~.Axes` at the top right corner of the current figure, occupying half of
      the figure height and a third of the figure width.
  
      .. note::
  
         Creating a subplot will delete any pre-existing subplot that overlaps
         with it beyond sharing a boundary::
  
            import matplotlib.pyplot as plt
            # plot a line, implicitly creating a subplot(111)
            plt.plot([1,2,3])
            # now create a subplot which represents the top plot of a grid
            # with 2 rows and 1 column. Since this subplot will overlap the
            # first, the plot (and its axes) previously created, will be removed
            plt.subplot(211)
            plt.plot(range(12))
            plt.subplot(212, facecolor='y') # creates 2nd subplot with yellow background
  
         If you do not want this behavior, use the
         :meth:`~matplotlib.figure.Figure.add_subplot` method or the
         :func:`~matplotlib.pyplot.axes` function instead.
  
      Keyword arguments:
  
        *facecolor*:
          The background color of the subplot, which can be any valid
          color specifier.  See :mod:`matplotlib.colors` for more
          information.
  
        *polar*:
          A boolean flag indicating whether the subplot plot should be
          a polar projection.  Defaults to *False*.
  
        *projection*:
          A string giving the name of a custom projection to be used
          for the subplot. This projection must have been previously
          registered. See :mod:`matplotlib.projections`.
  
      .. seealso::
  
          :func:`~matplotlib.pyplot.axes`
              For additional information on :func:`axes` and
              :func:`subplot` keyword arguments.
  
          :file:`gallery/pie_and_polar_charts/polar_scatter.py`
              For an example
  
      **Example:**
  
      .. plot:: gallery/subplots_axes_and_figures/subplot.py
  
      """
      # 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) :
          warnings.warn("The subplot index argument to subplot() appears"
                        " to be a boolean. Did you intend to use subplots()?")
  
      fig = gcf()
      a = fig.add_subplot(*args, **kwargs)
      bbox = a.bbox
      byebye = []
      for other in fig.axes:
          if other==a: continue
          if bbox.fully_overlaps(other.bbox):
              byebye.append(other)
      for ax in byebye: delaxes(ax)
  
      return a
  
  
  def subplots(nrows=1, ncols=1, sharex=False, sharey=False, squeeze=True,
               subplot_kw=None, gridspec_kw=None, **fig_kw):
      """
      Create a figure and a set of subplots
  
      This utility wrapper makes it convenient to create common layouts of
      subplots, including the enclosing figure object, in a single call.
  
      Parameters
      ----------
      nrows, ncols : int, optional, default: 1
          Number of rows/columns of the subplot grid.
  
      sharex, sharey : bool or {'none', 'all', 'row', 'col'}, default: False
          Controls sharing of properties among x (`sharex`) or y (`sharey`)
          axes:
  
              - True or 'all': x- or y-axis will be shared among all
                subplots.
              - False or 'none': each subplot x- or y-axis will be
                independent.
              - 'row': each subplot row will share an x- or y-axis.
              - 'col': each subplot column will share an x- or y-axis.
  
          When subplots have a shared x-axis along a column, only the x tick
          labels of the bottom subplot are visible.  Similarly, when subplots
          have a shared y-axis along a row, only the y tick labels of the first
          column subplot are visible.
  
      squeeze : bool, optional, default: True
          - If True, extra dimensions are squeezed out from the returned Axes
            object:
  
              - if only one subplot is constructed (nrows=ncols=1), the
                resulting single Axes object is returned as a scalar.
              - for Nx1 or 1xN subplots, the returned object is a 1D numpy
                object array of Axes objects are returned as numpy 1D arrays.
              - for NxM, subplots with N>1 and M>1 are returned as a 2D arrays.
  
          - 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
          :meth:`~matplotlib.figure.Figure.add_subplot` call used to create each
          subplot.
  
      gridspec_kw : dict, optional
          Dict with keywords passed to the
          :class:`~matplotlib.gridspec.GridSpec` constructor used to create the
          grid the subplots are placed on.
  
      **fig_kw :
          All additional keyword arguments are passed to the :func:`figure` call.
  
      Returns
      -------
      fig : :class:`matplotlib.figure.Figure` object
  
      ax : Axes object or array of Axes objects.
  
          ax can be either a single :class:`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.
  
      Examples
      --------
      First create some toy data:
  
      >>> x = np.linspace(0, 2*np.pi, 400)
      >>> y = np.sin(x**2)
  
      Creates just a figure and only one subplot
  
      >>> fig, ax = plt.subplots()
      >>> ax.plot(x, y)
      >>> ax.set_title('Simple plot')
  
      Creates two subplots and unpacks 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)
  
      Creates four polar axes, and accesses them through the returned array
  
      >>> fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True))
      >>> axes[0, 0].plot(x, y)
      >>> axes[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)
  
      See Also
      --------
      figure
      subplot
      """
      fig = figure(**fig_kw)
      axs = fig.subplots(nrows=nrows, ncols=ncols, sharex=sharex, sharey=sharey,
                         squeeze=squeeze, subplot_kw=subplot_kw,
                         gridspec_kw=gridspec_kw)
      return fig, axs
  
  
  def subplot2grid(shape, loc, rowspan=1, colspan=1, fig=None, **kwargs):
      """
      Create an axis at specific location inside a regular grid.
  
      Parameters
      ----------
      shape : sequence of 2 ints
          Shape of grid in which to place axis.
          First entry is number of rows, second entry is number of columns.
  
      loc : sequence of 2 ints
          Location to place axis within grid.
          First entry is row number, second entry is column number.
  
      rowspan : int
          Number of rows for the axis to span to the right.
  
      colspan : int
          Number of columns for the axis to span downwards.
  
      fig : `Figure`, optional
          Figure to place axis in. Defaults to current figure.
  
      **kwargs
          Additional keyword arguments are handed to `add_subplot`.
  
  
      Notes
      -----
      The following call ::
  
          subplot2grid(shape, loc, rowspan=1, colspan=1)
  
      is identical to ::
  
          gridspec=GridSpec(shape[0], shape[1])
          subplotspec=gridspec.new_subplotspec(loc, rowspan, colspan)
          subplot(subplotspec)
      """
  
      if fig is None:
          fig = gcf()
  
      s1, s2 = shape
      subplotspec = GridSpec(s1, s2).new_subplotspec(loc,
                                                     rowspan=rowspan,
                                                     colspan=colspan)
      a = fig.add_subplot(subplotspec, **kwargs)
      bbox = a.bbox
      byebye = []
      for other in fig.axes:
          if other == a:
              continue
          if bbox.fully_overlaps(other.bbox):
              byebye.append(other)
      for ax in byebye:
          delaxes(ax)
  
      return a
  
  
  def twinx(ax=None):
      """
      Make a second axes that shares the *x*-axis.  The new axes will
      overlay *ax* (or the current axes if *ax* is *None*).  The ticks
      for *ax2* will be placed on the right, and the *ax2* instance is
      returned.
  
      .. seealso::
  
         :file:`examples/api_examples/two_scales.py`
            For an example
      """
      if ax is None:
          ax=gca()
      ax1 = ax.twinx()
      return ax1
  
  
  def twiny(ax=None):
      """
      Make a second axes that shares the *y*-axis.  The new axis will
      overlay *ax* (or the current axes if *ax* is *None*).  The ticks
      for *ax2* will be placed on the top, and the *ax2* instance is
      returned.
      """
      if ax is None:
          ax=gca()
      ax1 = ax.twiny()
      return ax1
  
  
  def subplots_adjust(*args, **kwargs):
      """
      Tune the subplot layout.
  
      call signature::
  
        subplots_adjust(left=None, bottom=None, right=None, top=None,
                        wspace=None, hspace=None)
  
      The parameter meanings (and suggested defaults) are::
  
        left  = 0.125  # the left side of the subplots of the figure
        right = 0.9    # the right side of the subplots of the figure
        bottom = 0.1   # the bottom of the subplots of the figure
        top = 0.9      # the top of the subplots of the figure
        wspace = 0.2   # the amount of width reserved for blank space between subplots,
                       # expressed as a fraction of the average axis width
        hspace = 0.2   # the amount of height reserved for white space between subplots,
                       # expressed as a fraction of the average axis height
  
      The actual defaults are controlled by the rc file
      """
      fig = gcf()
      fig.subplots_adjust(*args, **kwargs)
  
  
  def subplot_tool(targetfig=None):
      """
      Launch a subplot tool window for a figure.
  
      A :class:`matplotlib.widgets.SubplotTool` instance is returned.
      """
      tbar = rcParams['toolbar'] # turn off the navigation toolbar for the toolfig
      rcParams['toolbar'] = 'None'
      if targetfig is None:
          manager = get_current_fig_manager()
          targetfig = manager.canvas.figure
      else:
          # find the manager for this figure
          for manager in _pylab_helpers.Gcf._activeQue:
              if manager.canvas.figure==targetfig: break
          else: raise RuntimeError('Could not find manager for targetfig')
  
      toolfig = figure(figsize=(6,3))
      toolfig.subplots_adjust(top=0.9)
      ret =  SubplotTool(targetfig, toolfig)
      rcParams['toolbar'] = tbar
      _pylab_helpers.Gcf.set_active(manager)  # restore the current figure
      return ret
  
  
  def tight_layout(pad=1.08, h_pad=None, w_pad=None, rect=None):
      """
      Automatically adjust subplot parameters to give specified padding.
  
      Parameters
      ----------
      pad : float
          padding between the figure edge and the edges of subplots, as a fraction of the font-size.
      h_pad, w_pad : float
          padding (height/width) between edges of adjacent subplots.
          Defaults to `pad_inches`.
      rect : if rect is given, it is interpreted as a rectangle
          (left, bottom, right, top) in the normalized figure
          coordinate that the whole subplots area (including
          labels) will fit into. Default is (0, 0, 1, 1).
  
      """
      fig = gcf()
      fig.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* may be a boolean or a string,
      'on' or 'off'.
  
      If *on* is *None*, toggle state.
      """
      ax = gca()
      on = _string_to_bool(on)
      if on is None:
          on = not ax.get_frame_on()
      ax.set_frame_on(on)
  
  
  def title(s, *args, **kwargs):
      """
      Set a title of the current axes.
  
      Set one of the three available axes titles. The available titles are
      positioned above the axes in the center, flush with the left edge,
      and flush with the right edge.
  
      .. seealso::
          See :func:`~matplotlib.pyplot.text` for adding text
          to the current axes
  
      Parameters
      ----------
      label : str
          Text to use for the title
  
      fontdict : dict
          A dictionary controlling the appearance of the title text,
          the default `fontdict` is:
  
              {'fontsize': rcParams['axes.titlesize'],
              'fontweight' : rcParams['axes.titleweight'],
              'verticalalignment': 'baseline',
              'horizontalalignment': loc}
  
      loc : {'center', 'left', 'right'}, str, optional
          Which title to set, defaults to 'center'
  
      Returns
      -------
      text : :class:`~matplotlib.text.Text`
          The matplotlib text instance representing the title
  
      Other parameters
      ----------------
      kwargs : text properties
          Other keyword arguments are text properties, see
          :class:`~matplotlib.text.Text` for a list of valid text
          properties.
  
      """
      return gca().set_title(s, *args, **kwargs, fontproperties = FontProperties(fname='/Library/Fonts/Songti.ttc'))
  
  ## Axis ##
  
  
  def axis(*v, **kwargs):
      """
      Convenience method to get or set axis properties.
  
      Calling with no arguments::
  
        >>> axis()
  
      returns the current axes limits ``[xmin, xmax, ymin, ymax]``.::
  
        >>> axis(v)
  
      sets the min and max of the x and y axes, with
      ``v = [xmin, xmax, ymin, ymax]``.::
  
        >>> axis('off')
  
      turns off the axis lines and labels.::
  
        >>> axis('equal')
  
      changes limits of *x* or *y* axis so that equal increments of *x*
      and *y* have the same length; a circle is circular.::
  
        >>> axis('scaled')
  
      achieves the same result by changing the dimensions of the plot box instead
      of the axis data limits.::
  
        >>> axis('tight')
  
      changes *x* and *y* axis limits such that all data is shown. If
      all data is already shown, it will move it to the center of the
      figure without modifying (*xmax* - *xmin*) or (*ymax* -
      *ymin*). Note this is slightly different than in MATLAB.::
  
        >>> axis('image')
  
      is 'scaled' with the axis limits equal to the data limits.::
  
        >>> axis('auto')
  
      and::
  
        >>> axis('normal')
  
      are deprecated. They restore default behavior; axis limits are automatically
      scaled to make the data fit comfortably within the plot box.
  
      if ``len(*v)==0``, you can pass in *xmin*, *xmax*, *ymin*, *ymax*
      as kwargs selectively to alter just those limits without changing
      the others.
  
        >>> axis('square')
  
      changes the limit ranges (*xmax*-*xmin*) and (*ymax*-*ymin*) of
      the *x* and *y* axes to be the same, and have the same scaling,
      resulting in a square plot.
  
      The xmin, xmax, ymin, ymax tuple is returned
  
      .. seealso::
  
          :func:`xlim`, :func:`ylim`
             For setting the x- and y-limits individually.
      """
      return gca().axis(*v, **kwargs)
  
  
  def xlabel(s, *args, **kwargs):
      """
      Set the *x* axis label of the current axis.
  
      Default override is::
  
        override = {
            'fontsize'            : 'small',
            'verticalalignment'   : 'top',
            'horizontalalignment' : 'center'
            }
  
      .. seealso::
  
          :func:`~matplotlib.pyplot.text`
              For information on how override and the optional args work
      """
      return gca().set_xlabel(s, *args, **kwargs, fontproperties = FontProperties(fname = '/Library/Fonts/Songti.ttc'))
  
  
  def ylabel(s, *args, **kwargs):
      """
      Set the *y* axis label of the current axis.
  
      Defaults override is::
  
          override = {
             'fontsize'            : 'small',
             'verticalalignment'   : 'center',
             'horizontalalignment' : 'right',
             'rotation'='vertical' : }
  
      .. seealso::
  
          :func:`~matplotlib.pyplot.text`
              For information on how override and the optional args
              work.
      """
      return gca().set_ylabel(s, *args, **kwargs, fontproperties = FontProperties(fname  = '/Library/Fonts/Songti.ttc'))
  
  
  def xlim(*args, **kwargs):
      """
      Get or set the *x* limits of the current axes.
  
      ::
  
        xmin, xmax = xlim()   # return the current xlim
        xlim( (xmin, xmax) )  # set the xlim to xmin, xmax
        xlim( xmin, xmax )    # set the xlim to xmin, xmax
  
      If you do not specify args, you can pass the xmin and xmax as
      kwargs, e.g.::
  
        xlim(xmax=3) # adjust the max leaving min unchanged
        xlim(xmin=1) # adjust the min leaving max unchanged
  
      Setting limits turns autoscaling off for the x-axis.
  
      The new axis limits are returned as a length 2 tuple.
  
      """
      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.
  
      ::
  
        ymin, ymax = ylim()   # return the current ylim
        ylim( (ymin, ymax) )  # set the ylim to ymin, ymax
        ylim( ymin, ymax )    # set the ylim to ymin, ymax
  
      If you do not specify args, you can pass the *ymin* and *ymax* as
      kwargs, e.g.::
  
        ylim(ymax=3) # adjust the max leaving min unchanged
        ylim(ymin=1) # adjust the min leaving max unchanged
  
      Setting limits turns autoscaling off for the y-axis.
  
      The new axis limits are returned as a length 2 tuple.
      """
      ax = gca()
      if not args and not kwargs:
          return ax.get_ylim()
      ret = ax.set_ylim(*args, **kwargs)
      return ret
  
  
  @docstring.dedent_interpd
  def xscale(*args, **kwargs):
      """
      Set the scaling of the *x*-axis.
  
      call signature::
  
        xscale(scale, **kwargs)
  
      The available scales are: %(scale)s
  
      Different keywords may be accepted, depending on the scale:
  
      %(scale_docs)s
      """
      gca().set_xscale(*args, **kwargs)
  
  
  @docstring.dedent_interpd
  def yscale(*args, **kwargs):
      """
      Set the scaling of the *y*-axis.
  
      call signature::
  
        yscale(scale, **kwargs)
  
      The available scales are: %(scale)s
  
      Different keywords may be accepted, depending on the scale:
  
      %(scale_docs)s
      """
      gca().set_yscale(*args, **kwargs)
  
  
  def xticks(*args, **kwargs):
      """
      Get or set the *x*-limits of the current tick locations and labels.
  
      ::
  
        # return locs, labels where locs is an array of tick locations and
        # labels is an array of tick labels.
        locs, labels = xticks()
  
        # set the locations of the xticks
        xticks( arange(6) )
  
        # set the locations and labels of the xticks
        xticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') )
  
      The keyword args, if any, are :class:`~matplotlib.text.Text`
      properties. For example, to rotate long labels::
  
        xticks( arange(12), calendar.month_name[1:13], rotation=17 )
      """
      ax = gca()
  
      if len(args)==0:
          locs = ax.get_xticks()
          labels = ax.get_xticklabels()
      elif len(args)==1:
          locs = ax.set_xticks(args[0])
          labels = ax.get_xticklabels()
      elif len(args)==2:
          locs = ax.set_xticks(args[0])
          labels = ax.set_xticklabels(args[1], **kwargs)
      else: raise TypeError('Illegal number of arguments to xticks')
      if len(kwargs):
          for l in labels:
              l.update(kwargs)
  
      return locs, silent_list('Text xticklabel', labels)
  
  
  def yticks(*args, **kwargs):
      """
      Get or set the *y*-limits of the current tick locations and labels.
  
      ::
  
        # return locs, labels where locs is an array of tick locations and
        # labels is an array of tick labels.
        locs, labels = yticks()
  
        # set the locations of the yticks
        yticks( arange(6) )
  
        # set the locations and labels of the yticks
        yticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') )
  
      The keyword args, if any, are :class:`~matplotlib.text.Text`
      properties. For example, to rotate long labels::
  
        yticks( arange(12), calendar.month_name[1:13], rotation=45 )
      """
      ax = gca()
  
      if len(args)==0:
          locs = ax.get_yticks()
          labels = ax.get_yticklabels()
      elif len(args)==1:
          locs = ax.set_yticks(args[0])
          labels = ax.get_yticklabels()
      elif len(args)==2:
          locs = ax.set_yticks(args[0])
          labels = ax.set_yticklabels(args[1], **kwargs)
      else: raise TypeError('Illegal number of arguments to yticks')
      if len(kwargs):
          for l in labels:
              l.update(kwargs)
  
  
      return ( locs,
               silent_list('Text yticklabel', labels)
               )
  
  
  def minorticks_on():
      """
      Display minor ticks on the current plot.
  
      Displaying minor ticks reduces performance; turn them off using
      minorticks_off() if drawing speed is a problem.
      """
      gca().minorticks_on()
  
  
  def minorticks_off():
      """
      Remove minor ticks from the current plot.
      """
      gca().minorticks_off()
  
  
  def rgrids(*args, **kwargs):
      """
      Get or set the radial gridlines on a polar plot.
  
      call signatures::
  
        lines, labels = rgrids()
        lines, labels = rgrids(radii, labels=None, angle=22.5, **kwargs)
  
      When called with no arguments, :func:`rgrid` simply returns the
      tuple (*lines*, *labels*), where *lines* is an array of radial
      gridlines (:class:`~matplotlib.lines.Line2D` instances) and
      *labels* is an array of tick labels
      (:class:`~matplotlib.text.Text` instances). When called with
      arguments, the labels will appear at the specified radial
      distances and angles.
  
      *labels*, if not *None*, is a len(*radii*) list of strings of the
      labels to use at each angle.
  
      If *labels* is None, the rformatter will be used
  
      Examples::
  
        # set the locations of the radial gridlines and labels
        lines, labels = rgrids( (0.25, 0.5, 1.0) )
  
        # set the locations and labels of the radial gridlines and labels
        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 len(args)==0:
          lines = ax.yaxis.get_gridlines()
          labels = ax.yaxis.get_ticklabels()
      else:
          lines, labels = ax.set_rgrids(*args, **kwargs)
  
      return ( silent_list('Line2D rgridline', lines),
               silent_list('Text rgridlabel', labels) )
  
  
  def thetagrids(*args, **kwargs):
      """
      Get or set the theta locations of the gridlines in a polar plot.
  
      If no arguments are passed, return a tuple (*lines*, *labels*)
      where *lines* is an array of radial gridlines
      (:class:`~matplotlib.lines.Line2D` instances) and *labels* is an
      array of tick labels (:class:`~matplotlib.text.Text` instances)::
  
        lines, labels = thetagrids()
  
      Otherwise the syntax is::
  
        lines, labels = thetagrids(angles, labels=None, fmt='%d', frac = 1.1)
  
      set the angles at which to place the theta grids (these gridlines
      are equal along the theta dimension).
  
      *angles* is in degrees.
  
      *labels*, if not *None*, is a len(angles) list of strings of the
      labels to use at each angle.
  
      If *labels* is *None*, the labels will be ``fmt%angle``.
  
      *frac* is the fraction of the polar axes radius at which to place
      the label (1 is the edge). e.g., 1.05 is outside the axes and 0.95
      is inside the axes.
  
      Return value is a list of tuples (*lines*, *labels*):
  
        - *lines* are :class:`~matplotlib.lines.Line2D` instances
  
        - *labels* are :class:`~matplotlib.text.Text` instances.
  
      Note that on input, the *labels* argument is a list of strings,
      and on output it is a list of :class:`~matplotlib.text.Text`
      instances.
  
      Examples::
  
        # set the locations of the radial gridlines and labels
        lines, labels = thetagrids( range(45,360,90) )
  
        # set the locations and labels of the radial gridlines and labels
        lines, labels = thetagrids( range(45,360,90), ('NE', 'NW', 'SW','SE') )
      """
      ax = gca()
      if not isinstance(ax, PolarAxes):
          raise RuntimeError('rgrids only defined for polar axes')
      if len(args)==0:
          lines = ax.xaxis.get_ticklines()
          labels = ax.xaxis.get_ticklabels()
      else:
          lines, labels = ax.set_thetagrids(*args, **kwargs)
  
      return (silent_list('Line2D thetagridline', lines),
              silent_list('Text thetagridlabel', 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.
  
      import inspect
  
      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)
  
      commands = set()
      for name, obj in list(six.iteritems(globals())):
          if name.startswith('_') or name in exclude:
              continue
          if inspect.isfunction(obj) and inspect.getmodule(obj) is this_module:
              commands.add(name)
  
      return sorted(commands)
  
  
  @deprecated('2.1')
  def colors():
      """
      This is a do-nothing function to provide you with help on how
      matplotlib handles colors.
  
      Commands which take color arguments can use several formats to
      specify the colors.  For the basic built-in colors, you can use a
      single letter
  
        =====   =======
        Alias   Color
        =====   =======
        'b'     blue
        'g'     green
        'r'     red
        'c'     cyan
        'm'     magenta
        'y'     yellow
        'k'     black
        'w'     white
        =====   =======
  
      For a greater range of colors, you have two options.  You can
      specify the color using an html hex string, as in::
  
        color = '#eeefff'
  
      or you can pass an R,G,B tuple, where each of R,G,B are in the
      range [0,1].
  
      You can also use any legal html name for a color, for example::
  
        color = 'red'
        color = 'burlywood'
        color = 'chartreuse'
  
      The example below creates a subplot with a dark
      slate gray background::
  
         subplot(111, facecolor=(0.1843, 0.3098, 0.3098))
  
      Here is an example that creates a pale turquoise title::
  
        title('Is this the best color?', color='#afeeee')
  
      """
      pass
  
  
  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
        meant for plotting values that wrap around at the
        endpoints, such as phase angle, wind direction, or time of day
      Qualitative schemes
        for nominal data that has no inherent ordering, where color is used
        only to distinguish categories
  
      Matplotlib ships with 4 perceptually uniform color maps which are
      the recommended color maps for sequential data:
  
        =========   ===================================================
        Colormap    Description
        =========   ===================================================
        inferno     perceptually uniform shades of black-red-yellow
        magma       perceptually uniform shades of black-red-white
        plasma      perceptually uniform shades of blue-red-yellow
        viridis     perceptually uniform shades of blue-green-yellow
        =========   ===================================================
  
      The following colormaps are based on the `ColorBrewer
      <http://colorbrewer2.org>`_ color specifications and designs developed by
      Cynthia Brewer:
  
      ColorBrewer Diverging (luminance is highest at the midpoint, and
      decreases towards differently-colored endpoints):
  
        ========  ===================================
        Colormap  Description
        ========  ===================================
        BrBG      brown, white, blue-green
        PiYG      pink, white, yellow-green
        PRGn      purple, white, green
        PuOr      orange, white, purple
        RdBu      red, white, blue
        RdGy      red, white, gray
        RdYlBu    red, yellow, blue
        RdYlGn    red, yellow, green
        Spectral  red, orange, yellow, green, blue
        ========  ===================================
  
      ColorBrewer Sequential (luminance decreases monotonically):
  
        ========  ====================================
        Colormap  Description
        ========  ====================================
        Blues     white to dark blue
        BuGn      white, light blue, dark green
        BuPu      white, light blue, dark purple
        GnBu      white, light green, dark blue
        Greens    white to dark green
        Greys     white to black (not linear)
        Oranges   white, orange, dark brown
        OrRd      white, orange, dark red
        PuBu      white, light purple, dark blue
        PuBuGn    white, light purple, dark green
        PuRd      white, light purple, dark red
        Purples   white to dark purple
        RdPu      white, pink, dark purple
        Reds      white to dark red
        YlGn      light yellow, dark green
        YlGnBu    light yellow, light green, dark blue
        YlOrBr    light yellow, orange, dark brown
        YlOrRd    light yellow, orange, dark red
        ========  ====================================
  
      ColorBrewer Qualitative:
  
      (For plotting nominal data, :class:`ListedColormap` is used,
      not :class:`LinearSegmentedColormap`.  Different sets of colors are
      recommended for different numbers of categories.)
  
      * Accent
      * Dark2
      * Paired
      * Pastel1
      * Pastel2
      * Set1
      * Set2
      * Set3
  
      A set of colormaps derived from those of the same name provided
      with Matlab are also included:
  
        =========   =======================================================
        Colormap    Description
        =========   =======================================================
        autumn      sequential linearly-increasing shades of red-orange-yellow
        bone        sequential increasing black-white color map with
                    a tinge of blue, to emulate X-ray film
        cool        linearly-decreasing shades of cyan-magenta
        copper      sequential increasing shades of black-copper
        flag        repetitive red-white-blue-black pattern (not cyclic at
                    endpoints)
        gray        sequential linearly-increasing black-to-white
                    grayscale
        hot         sequential black-red-yellow-white, to emulate blackbody
                    radiation from an object at increasing temperatures
        hsv         cyclic red-yellow-green-cyan-blue-magenta-red, formed
                    by changing the hue component in the HSV color space
        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
        ============  =======================================================
  
  
      Other miscellaneous schemes:
  
        ============= =======================================================
        Colormap      Description
        ============= =======================================================
        afmhot        sequential black-orange-yellow-white blackbody
                      spectrum, commonly used in atomic force microscopy
        brg           blue-red-green
        bwr           diverging blue-white-red
        coolwarm      diverging blue-gray-red, meant to avoid issues with 3D
                      shading, color blindness, and ordering of colors [#]_
        CMRmap        "Default colormaps on color images often reproduce to
                      confusing grayscale images. The proposed colormap
                      maintains an aesthetically pleasing color image that
                      automatically reproduces to a monotonic grayscale with
                      discrete, quantifiable saturation levels." [#]_
        cubehelix     Unlike most other color schemes cubehelix was designed
                      by D.A. Green to be monotonically increasing in terms
                      of perceived brightness. Also, when printed on a black
                      and white postscript printer, the scheme results in a
                      greyscale with monotonically increasing brightness.
                      This color scheme is named cubehelix because the r,g,b
                      values produced can be visualised as a squashed helix
                      around the diagonal in the r,g,b color cube.
        gnuplot       gnuplot's traditional pm3d scheme
                      (black-blue-red-yellow)
        gnuplot2      sequential color printable as gray
                      (black-blue-violet-yellow-white)
        ocean         green-blue-white
        rainbow       spectral purple-blue-green-yellow-orange-red colormap
                      with diverging luminance
        seismic       diverging blue-white-red
        nipy_spectral black-purple-blue-green-yellow-red-white spectrum,
                      originally from the Neuroimaging in Python project
        terrain       mapmaker's colors, blue-green-yellow-brown-white,
                      originally from IGOR Pro
        ============= =======================================================
  
      The following colormaps are redundant and may be removed in future
      versions.  It's recommended to use the names in the descriptions
      instead, which produce identical output:
  
        =========  =======================================================
        Colormap   Description
        =========  =======================================================
        gist_gray  identical to *gray*
        gist_yarg  identical to *gray_r*
        binary     identical to *gray_r*
        spectral   identical to *nipy_spectral* [#]_
        =========  =======================================================
  
      .. rubric:: Footnotes
  
      .. [#] Rainbow colormaps, ``jet`` in particular, are considered a poor
        choice for scientific visualization by many researchers: `Rainbow Color
        Map (Still) Considered Harmful
        <http://ieeexplore.ieee.org/document/4118486/?arnumber=4118486>`_
  
      .. [#] Resembles "BkBlAqGrYeOrReViWh200" from NCAR Command
        Language. See `Color Table Gallery
        <https://www.ncl.ucar.edu/Document/Graphics/color_table_gallery.shtml>`_
  
      .. [#] See `Diverging Color Maps for Scientific Visualization
        <http://www.kennethmoreland.com/color-maps/>`_ by Kenneth Moreland.
  
      .. [#] See `A Color Map for Effective Black-and-White Rendering of
        Color-Scale Images
        <https://www.mathworks.com/matlabcentral/fileexchange/2662-cmrmap-m>`_
        by Carey Rappaport
  
      .. [#] Changed to distinguish from ColorBrewer's *Spectral* map.
        :func:`spectral` still works, but
        ``set_cmap('nipy_spectral')`` is recommended for clarity.
  
  
      """
      return sorted(cm.cmap_d)
  
  
  def _setup_pyplot_info_docstrings():
      """
      Generates the plotting and docstring.
  
      These must be done after the entire module is imported, so it is
      called from the end of this module, which is generated by
      boilerplate.py.
      """
      # Generate the plotting docstring
      import re
  
      def pad(s, l):
          """Pad string *s* to length *l*."""
          if l < len(s):
              return s[:l]
          return s + ' ' * (l - len(s))
  
      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 = 0
      max_summary = 0
      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 = match.group(0).strip().replace('\n', ' ')
          name = '`%s`' % name
          rows.append([name, summary])
          max_name = max(max_name, len(name))
          max_summary = max(max_summary, len(summary))
  
      lines = []
      sep = '=' * max_name + ' ' + '=' * max_summary
      lines.append(sep)
      lines.append(' '.join([pad("Function", max_name),
                             pad("Description", max_summary)]))
      lines.append(sep)
      for name, summary in rows:
          lines.append(' '.join([pad(name, max_name),
                                 pad(summary, max_summary)]))
      lines.append(sep)
  
      plotting.__doc__ = '\n'.join(lines)
  
  ## Plotting part 1: manually generated functions and wrappers ##
  
  def colorbar(mappable=None, cax=None, ax=None, **kw):
      if mappable is None:
          mappable = gci()
          if mappable is None:
              raise RuntimeError('No mappable was found to use for colorbar '
                                 'creation. First define a mappable such as '
                                 'an image (with imshow) or a contour set ('
                                 'with contourf).')
      if ax is None:
          ax = gca()
  
      ret = gcf().colorbar(mappable, cax = cax, ax=ax, **kw)
      return ret
  colorbar.__doc__ = matplotlib.colorbar.colorbar_doc
  
  
  def clim(vmin=None, vmax=None):
      """
      Set the color limits of the current image.
  
      To apply clim to all axes images do::
  
        clim(0, 0.5)
  
      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, for example::
  
        for im in gca().get_images():
            im.set_clim(0, 0.05)
  
      """
      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.  Applies to the current image if any.
      See help(colormaps) for more information.
  
      *cmap* must be a :class:`~matplotlib.colors.Colormap` instance, or
      the name of a registered colormap.
  
      See :func:`matplotlib.cm.register_cmap` and
      :func:`matplotlib.cm.get_cmap`.
      """
      cmap = cm.get_cmap(cmap)
  
      rc('image', cmap=cmap.name)
      im = gci()
  
      if im is not None:
          im.set_cmap(cmap)
  
  
  
  @docstring.copy_dedent(_imread)
  def imread(*args, **kwargs):
      return _imread(*args, **kwargs)
  
  
  @docstring.copy_dedent(_imsave)
  def imsave(*args, **kwargs):
      return _imsave(*args, **kwargs)
  
  
  def matshow(A, fignum=None, **kw):
      """
      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.
  
      With the exception of *fignum*, keyword arguments are passed to
      :func:`~matplotlib.pyplot.imshow`.  You may set the *origin*
      kwarg to "lower" if you want the first row in the array to be
      at the bottom instead of the top.
  
  
      *fignum*: [ None | integer | False ]
        By default, :func:`matshow` creates a new figure window with
        automatic numbering.  If *fignum* is given as an integer, the
        created figure will use this figure number.  Because of how
        :func:`matshow` tries to set the figure aspect ratio to be the
        one of the array, if you provide the number of an already
        existing figure, strange things may happen.
  
        If *fignum* is *False* or 0, a new figure window will **NOT** be created.
      """
      A = np.asanyarray(A)
      if fignum is False or fignum is 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, **kw)
      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 :func:`~matplotlib.pyplot.plot`.
  
      """
      # If an axis already exists, check if it has a polar projection
      if gcf().get_axes():
          if not isinstance(gca(), PolarAxes):
              warnings.warn('Trying to create polar plot on an axis that does '
                            'not have a polar projection.')
      ax = gca(polar=True)
      ret = ax.plot(*args, **kwargs)
      return ret
  
  
  def plotfile(fname, cols=(0,), plotfuncs=None,
               comments='#', skiprows=0, checkrows=5, delimiter=',',
               names=None, subplots=True, newfig=True, **kwargs):
      """
      Plot the data in a file.
  
      *cols* is a sequence of column identifiers to plot.  An identifier
      is either an int or a string.  If it is an int, it indicates the
      column number.  If it is a string, it indicates the column header.
      matplotlib will make column headers lower case, replace spaces with
      underscores, and remove all illegal characters; so ``'Adj Close*'``
      will have name ``'adj_close'``.
  
      - If len(*cols*) == 1, only that column will be plotted on the *y* axis.
  
      - If len(*cols*) > 1, the first element will be an identifier for
        data for the *x* axis and the remaining elements will be the
        column indexes for multiple subplots if *subplots* is *True*
        (the default), or for lines in a single subplot if *subplots*
        is *False*.
  
      *plotfuncs*, if not *None*, is a dictionary mapping identifier to
      an :class:`~matplotlib.axes.Axes` plotting function as a string.
      Default is 'plot', other choices are 'semilogy', 'fill', 'bar',
      etc.  You must use the same type of identifier in the *cols*
      vector as you use in the *plotfuncs* dictionary, e.g., integer
      column numbers in both or column names in both. If *subplots*
      is *False*, then including any function such as 'semilogy'
      that changes the axis scaling will set the scaling for all
      columns.
  
      *comments*, *skiprows*, *checkrows*, *delimiter*, and *names*
      are all passed on to :func:`matplotlib.pylab.csv2rec` to
      load the data into a record array.
  
      If *newfig* is *True*, the plot always will be made in a new figure;
      if *False*, it will be made in the current figure if one exists,
      else in a new figure.
  
      kwargs are passed on to plotting functions.
  
      Example usage::
  
        # plot the 2nd and 4th column against the 1st in two subplots
        plotfile(fname, (0,1,3))
  
        # plot using column names; specify an alternate plot type for volume
        plotfile(fname, ('date', 'volume', 'adj_close'),
                                      plotfuncs={'volume': 'semilogy'})
  
      Note: plotfile is intended as a convenience for quickly plotting
      data from flat files; it is not intended as an alternative
      interface to general plotting with pyplot or matplotlib.
      """
  
      if newfig:
          fig = figure()
      else:
          fig = gcf()
  
      if len(cols)<1:
          raise ValueError('must have at least one column of data')
  
      if plotfuncs is None:
          plotfuncs = dict()
      r = mlab.csv2rec(fname, comments=comments, skiprows=skiprows,
                       checkrows=checkrows, delimiter=delimiter, names=names)
  
      def getname_val(identifier):
          'return the name and column data for identifier'
          if isinstance(identifier, six.string_types):
              return identifier, r[identifier]
          elif is_numlike(identifier):
              name = r.dtype.names[int(identifier)]
              return name, r[name]
          else:
              raise TypeError('identifier must be a string or integer')
  
      xname, x = getname_val(cols[0])
      ynamelist = []
  
      if len(cols)==1:
          ax1 = fig.add_subplot(1,1,1)
          funcname = plotfuncs.get(cols[0], 'plot')
          func = getattr(ax1, funcname)
          func(x, **kwargs)
          ax1.set_ylabel(xname)
      else:
          N = len(cols)
          for i in range(1,N):
              if subplots:
                  if i==1:
                      ax = ax1 = fig.add_subplot(N-1,1,i)
                  else:
                      ax = fig.add_subplot(N-1,1,i, sharex=ax1)
              elif i==1:
                  ax = fig.add_subplot(1,1,1)
  
              yname, y = getname_val(cols[i])
              ynamelist.append(yname)
  
              funcname = plotfuncs.get(cols[i], 'plot')
              func = getattr(ax, funcname)
  
              func(x, y, **kwargs)
              if subplots:
                  ax.set_ylabel(yname)
              if ax.is_last_row():
                  ax.set_xlabel(xname)
              else:
                  ax.set_xlabel('')
  
      if not subplots:
          ax.legend(ynamelist, loc='best')
  
      if xname=='date':
          fig.autofmt_xdate()
  
  
  def _autogen_docstring(base):
      """Autogenerated wrappers will get their docstring from a base function
      with an addendum."""
      #msg = "\n\nAdditional kwargs: hold = [True|False] overrides default hold state"
      msg = ''
      addendum = docstring.Appender(msg, '\n\n')
      return lambda func: addendum(docstring.copy_dedent(base)(func))
  
  # This function cannot be generated by boilerplate.py because it may
  # return an image or a line.
  @_autogen_docstring(Axes.spy)
  def spy(Z, precision=0, marker=None, markersize=None, aspect='equal', **kwargs):
      ax = gca()
      hold = kwargs.pop('hold', None)
      # allow callers to override the hold state by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.spy(Z, precision, marker, markersize, aspect, **kwargs)
      finally:
          ax._hold = washold
      if isinstance(ret, cm.ScalarMappable):
          sci(ret)
      return ret
  
  # 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 ##############
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.acorr)
  def acorr(x, hold=None, data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.acorr(x, data=data, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.angle_spectrum)
  def angle_spectrum(x, Fs=None, Fc=None, window=None, pad_to=None, sides=None,
                     hold=None, data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.angle_spectrum(x, Fs=Fs, Fc=Fc, window=window, pad_to=pad_to,
                                  sides=sides, data=data, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.arrow)
  def arrow(x, y, dx, dy, hold=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.arrow(x, y, dx, dy, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.axhline)
  def axhline(y=0, xmin=0, xmax=1, hold=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.axhline(y=y, xmin=xmin, xmax=xmax, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.axhspan)
  def axhspan(ymin, ymax, xmin=0, xmax=1, hold=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.axhspan(ymin, ymax, xmin=xmin, xmax=xmax, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.axvline)
  def axvline(x=0, ymin=0, ymax=1, hold=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.axvline(x=x, ymin=ymin, ymax=ymax, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.axvspan)
  def axvspan(xmin, xmax, ymin=0, ymax=1, hold=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.axvspan(xmin, xmax, ymin=ymin, ymax=ymax, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.bar)
  def bar(*args, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kwargs.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.bar(*args, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.barh)
  def barh(*args, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kwargs.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.barh(*args, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.broken_barh)
  def broken_barh(xranges, yrange, hold=None, data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.broken_barh(xranges, yrange, data=data, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(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_xticks=True, autorange=False, zorder=None,
              hold=None, data=None):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.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_xticks=manage_xticks, autorange=autorange,
                           zorder=zorder, data=data)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(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, hold=None, data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.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,
                          **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.clabel)
  def clabel(CS, *args, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kwargs.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.clabel(CS, *args, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.contour)
  def contour(*args, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kwargs.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.contour(*args, **kwargs)
      finally:
          ax._hold = washold
      if ret._A is not None: sci(ret)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.contourf)
  def contourf(*args, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kwargs.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.contourf(*args, **kwargs)
      finally:
          ax._hold = washold
      if ret._A is not None: sci(ret)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(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, hold=None, data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.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, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(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,
               hold=None, data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.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,
                            **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.eventplot)
  def eventplot(positions, orientation='horizontal', lineoffsets=1, linelengths=1,
                linewidths=None, colors=None, linestyles='solid', hold=None,
                data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.eventplot(positions, orientation=orientation,
                             lineoffsets=lineoffsets, linelengths=linelengths,
                             linewidths=linewidths, colors=colors,
                             linestyles=linestyles, data=data, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.fill)
  def fill(*args, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kwargs.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.fill(*args, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.fill_between)
  def fill_between(x, y1, y2=0, where=None, interpolate=False, step=None,
                   hold=None, data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.fill_between(x, y1, y2=y2, where=where,
                                interpolate=interpolate, step=step, data=data,
                                **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.fill_betweenx)
  def fill_betweenx(y, x1, x2=0, where=None, step=None, interpolate=False,
                    hold=None, data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.fill_betweenx(y, x1, x2=x2, where=where, step=step,
                                 interpolate=interpolate, data=data, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(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, hold=None,
             data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.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, **kwargs)
      finally:
          ax._hold = washold
      sci(ret)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.hist)
  def hist(x, bins=None, range=None, density=None, weights=None, cumulative=False,
           bottom=None, histtype='bar', align='mid', orientation='vertical',
           rwidth=None, log=False, color=None, label=None, stacked=False,
           normed=None, hold=None, data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.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, normed=normed, data=data, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.hist2d)
  def hist2d(x, y, bins=10, range=None, normed=False, weights=None, cmin=None,
             cmax=None, hold=None, data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.hist2d(x, y, bins=bins, range=range, normed=normed,
                          weights=weights, cmin=cmin, cmax=cmax, data=data,
                          **kwargs)
      finally:
          ax._hold = washold
      sci(ret[-1])
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.hlines)
  def hlines(y, xmin, xmax, colors='k', linestyles='solid', label='', hold=None,
             data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.hlines(y, xmin, xmax, colors=colors, linestyles=linestyles,
                          label=label, data=data, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.imshow)
  def imshow(X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=None,
             vmin=None, vmax=None, origin=None, extent=None, shape=None,
             filternorm=1, filterrad=4.0, imlim=None, resample=None, url=None,
             hold=None, data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.imshow(X, cmap=cmap, norm=norm, aspect=aspect,
                          interpolation=interpolation, alpha=alpha, vmin=vmin,
                          vmax=vmax, origin=origin, extent=extent, shape=shape,
                          filternorm=filternorm, filterrad=filterrad,
                          imlim=imlim, resample=resample, url=url, data=data,
                          **kwargs)
      finally:
          ax._hold = washold
      sci(ret)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.loglog)
  def loglog(*args, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kwargs.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.loglog(*args, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.magnitude_spectrum)
  def magnitude_spectrum(x, Fs=None, Fc=None, window=None, pad_to=None,
                         sides=None, scale=None, hold=None, data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.magnitude_spectrum(x, Fs=Fs, Fc=Fc, window=window,
                                      pad_to=pad_to, sides=sides, scale=scale,
                                      data=data, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.pcolor)
  def pcolor(*args, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kwargs.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.pcolor(*args, **kwargs)
      finally:
          ax._hold = washold
      sci(ret)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.pcolormesh)
  def pcolormesh(*args, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kwargs.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.pcolormesh(*args, **kwargs)
      finally:
          ax._hold = washold
      sci(ret)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.phase_spectrum)
  def phase_spectrum(x, Fs=None, Fc=None, window=None, pad_to=None, sides=None,
                     hold=None, data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.phase_spectrum(x, Fs=Fs, Fc=Fc, window=window, pad_to=pad_to,
                                  sides=sides, data=data, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.pie)
  def pie(x, explode=None, labels=None, colors=None, autopct=None,
          pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=None,
          radius=None, counterclock=True, wedgeprops=None, textprops=None,
          center=(0, 0), frame=False, rotatelabels=False, hold=None, data=None):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.pie(x, explode=explode, labels=labels, colors=colors,
                       autopct=autopct, pctdistance=pctdistance, shadow=shadow,
                       labeldistance=labeldistance, startangle=startangle,
                       radius=radius, counterclock=counterclock,
                       wedgeprops=wedgeprops, textprops=textprops, center=center,
                       frame=frame, rotatelabels=rotatelabels, data=data)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.plot)
  def plot(*args, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kwargs.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.plot(*args, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.plot_date)
  def plot_date(x, y, fmt='o', tz=None, xdate=True, ydate=False, hold=None,
                data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.plot_date(x, y, fmt=fmt, tz=tz, xdate=xdate, ydate=ydate,
                             data=data, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(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, hold=None, data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.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, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.quiver)
  def quiver(*args, **kw):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kw.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.quiver(*args, **kw)
      finally:
          ax._hold = washold
      sci(ret)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.quiverkey)
  def quiverkey(*args, **kw):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kw.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.quiverkey(*args, **kw)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.scatter)
  def scatter(x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None,
              vmax=None, alpha=None, linewidths=None, verts=None, edgecolors=None,
              hold=None, data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.scatter(x, y, s=s, c=c, marker=marker, cmap=cmap, norm=norm,
                           vmin=vmin, vmax=vmax, alpha=alpha,
                           linewidths=linewidths, verts=verts,
                           edgecolors=edgecolors, data=data, **kwargs)
      finally:
          ax._hold = washold
      sci(ret)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.semilogx)
  def semilogx(*args, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kwargs.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.semilogx(*args, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.semilogy)
  def semilogy(*args, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kwargs.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.semilogy(*args, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(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,
               hold=None, data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.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, **kwargs)
      finally:
          ax._hold = washold
      sci(ret[-1])
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.stackplot)
  def stackplot(x, *args, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kwargs.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.stackplot(x, *args, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.stem)
  def stem(*args, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kwargs.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.stem(*args, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.step)
  def step(x, y, *args, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kwargs.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.step(x, y, *args, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(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', hold=None, data=None):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.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)
      finally:
          ax._hold = washold
      sci(ret.lines)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.tricontour)
  def tricontour(*args, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kwargs.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.tricontour(*args, **kwargs)
      finally:
          ax._hold = washold
      if ret._A is not None: sci(ret)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.tricontourf)
  def tricontourf(*args, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kwargs.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.tricontourf(*args, **kwargs)
      finally:
          ax._hold = washold
      if ret._A is not None: sci(ret)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.tripcolor)
  def tripcolor(*args, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kwargs.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.tripcolor(*args, **kwargs)
      finally:
          ax._hold = washold
      sci(ret)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.triplot)
  def triplot(*args, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kwargs.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.triplot(*args, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.violinplot)
  def violinplot(dataset, positions=None, vert=True, widths=0.5, showmeans=False,
                 showextrema=True, showmedians=False, points=100, bw_method=None,
                 hold=None, data=None):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.violinplot(dataset, positions=positions, vert=vert,
                              widths=widths, showmeans=showmeans,
                              showextrema=showextrema, showmedians=showmedians,
                              points=points, bw_method=bw_method, data=data)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.vlines)
  def vlines(x, ymin, ymax, colors='k', linestyles='solid', label='', hold=None,
             data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.vlines(x, ymin, ymax, colors=colors, linestyles=linestyles,
                          label=label, data=data, **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.xcorr)
  def xcorr(x, y, normed=True, detrend=mlab.detrend_none, usevlines=True,
            maxlags=10, hold=None, data=None, **kwargs):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
  
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.xcorr(x, y, normed=normed, detrend=detrend,
                         usevlines=usevlines, maxlags=maxlags, data=data,
                         **kwargs)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @_autogen_docstring(Axes.barbs)
  def barbs(*args, **kw):
      ax = gca()
      # Deprecated: allow callers to override the hold state
      # by passing hold=True|False
      washold = ax._hold
      hold = kw.pop('hold', None)
      if hold is not None:
          ax._hold = hold
          from matplotlib.cbook import mplDeprecation
          warnings.warn("The 'hold' keyword argument is deprecated since 2.0.",
                        mplDeprecation)
      try:
          ret = ax.barbs(*args, **kw)
      finally:
          ax._hold = washold
  
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @docstring.copy_dedent(Axes.cla)
  def cla():
      ret = gca().cla()
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @docstring.copy_dedent(Axes.grid)
  def grid(b=None, which='major', axis='both', **kwargs):
      ret = gca().grid(b=b, which=which, axis=axis, **kwargs)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @docstring.copy_dedent(Axes.legend)
  def legend(*args, **kwargs):
      ret = gca().legend(*args, **kwargs, fontproperties = FontProperties(fname  = '/Library/Fonts/Songti.ttc'))
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @docstring.copy_dedent(Axes.table)
  def table(**kwargs):
      ret = gca().table(**kwargs)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @docstring.copy_dedent(Axes.text)
  def text(x, y, s, fontdict=None, withdash=False, **kwargs):
      ret = gca().text(x, y, s, fontdict=fontdict, withdash=withdash, **kwargs, fontproperties = FontProperties(fname  = '/Library/Fonts/Songti.ttc'))
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @docstring.copy_dedent(Axes.annotate)
  def annotate(*args, **kwargs):
      ret = gca().annotate(*args, **kwargs)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @docstring.copy_dedent(Axes.ticklabel_format)
  def ticklabel_format(**kwargs):
      ret = gca().ticklabel_format(**kwargs)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @docstring.copy_dedent(Axes.locator_params)
  def locator_params(axis='both', tight=None, **kwargs):
      ret = gca().locator_params(axis=axis, tight=tight, **kwargs)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @docstring.copy_dedent(Axes.tick_params)
  def tick_params(axis='both', **kwargs):
      ret = gca().tick_params(axis=axis, **kwargs)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @docstring.copy_dedent(Axes.margins)
  def margins(*args, **kw):
      ret = gca().margins(*args, **kw)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  @docstring.copy_dedent(Axes.autoscale)
  def autoscale(enable=True, axis='both', tight=None):
      ret = gca().autoscale(enable=enable, axis=axis, tight=tight)
      return ret
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def autumn():
      '''
      set the default colormap to autumn and apply to current image if any.
      See help(colormaps) for more information
      '''
      rc('image', cmap='autumn')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.autumn)
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def bone():
      '''
      set the default colormap to bone and apply to current image if any.
      See help(colormaps) for more information
      '''
      rc('image', cmap='bone')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.bone)
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def cool():
      '''
      set the default colormap to cool and apply to current image if any.
      See help(colormaps) for more information
      '''
      rc('image', cmap='cool')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.cool)
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def copper():
      '''
      set the default colormap to copper and apply to current image if any.
      See help(colormaps) for more information
      '''
      rc('image', cmap='copper')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.copper)
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def flag():
      '''
      set the default colormap to flag and apply to current image if any.
      See help(colormaps) for more information
      '''
      rc('image', cmap='flag')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.flag)
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def gray():
      '''
      set the default colormap to gray and apply to current image if any.
      See help(colormaps) for more information
      '''
      rc('image', cmap='gray')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.gray)
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def hot():
      '''
      set the default colormap to hot and apply to current image if any.
      See help(colormaps) for more information
      '''
      rc('image', cmap='hot')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.hot)
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def hsv():
      '''
      set the default colormap to hsv and apply to current image if any.
      See help(colormaps) for more information
      '''
      rc('image', cmap='hsv')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.hsv)
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def jet():
      '''
      set the default colormap to jet and apply to current image if any.
      See help(colormaps) for more information
      '''
      rc('image', cmap='jet')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.jet)
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def pink():
      '''
      set the default colormap to pink and apply to current image if any.
      See help(colormaps) for more information
      '''
      rc('image', cmap='pink')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.pink)
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def prism():
      '''
      set the default colormap to prism and apply to current image if any.
      See help(colormaps) for more information
      '''
      rc('image', cmap='prism')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.prism)
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def spring():
      '''
      set the default colormap to spring and apply to current image if any.
      See help(colormaps) for more information
      '''
      rc('image', cmap='spring')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.spring)
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def summer():
      '''
      set the default colormap to summer and apply to current image if any.
      See help(colormaps) for more information
      '''
      rc('image', cmap='summer')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.summer)
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def winter():
      '''
      set the default colormap to winter and apply to current image if any.
      See help(colormaps) for more information
      '''
      rc('image', cmap='winter')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.winter)
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def magma():
      '''
      set the default colormap to magma and apply to current image if any.
      See help(colormaps) for more information
      '''
      rc('image', cmap='magma')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.magma)
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def inferno():
      '''
      set the default colormap to inferno and apply to current image if any.
      See help(colormaps) for more information
      '''
      rc('image', cmap='inferno')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.inferno)
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def plasma():
      '''
      set the default colormap to plasma and apply to current image if any.
      See help(colormaps) for more information
      '''
      rc('image', cmap='plasma')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.plasma)
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def viridis():
      '''
      set the default colormap to viridis and apply to current image if any.
      See help(colormaps) for more information
      '''
      rc('image', cmap='viridis')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.viridis)
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def nipy_spectral():
      '''
      set the default colormap to nipy_spectral and apply to current image if any.
      See help(colormaps) for more information
      '''
      rc('image', cmap='nipy_spectral')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.nipy_spectral)
  
  
  # This function was autogenerated by boilerplate.py.  Do not edit as
  # changes will be lost
  def spectral():
      '''
      set the default colormap to spectral and apply to current image if any.
      See help(colormaps) for more information
      '''
      from matplotlib.cbook import warn_deprecated
      warn_deprecated(
                      "2.0",
                      name="spectral",
                      obj_type="colormap"
                      )
  
      rc('image', cmap='spectral')
      im = gci()
  
      if im is not None:
          im.set_cmap(cm.spectral)
  
  _setup_pyplot_info_docstrings()
  