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# Note: The first part of this file can be modified in place, but the latter
# part is autogenerated by the boilerplate.py script.
"""
Provides a MATLAB-like plotting framework.
:mod:`~matplotlib.pylab` combines pyplot with numpy into a single namespace.
This is convenient for interactive work, but for programming it
is recommended that the namespaces be kept separate, e.g.::
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 5, 0.1);
y = np.sin(x)
plt.plot(x, y)
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import sys
import warnings
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_string_like, 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
## 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,
and the GUI event loop will run during the pause.
If there is no active figure, or if a non-interactive backend
is in use, this executes time.sleep(interval).
This can be used for crude animation. For more complex
animation, see :mod:`matplotlib.animation`.
This function is experimental; its behavior may be changed
or extended in a future release.
"""
backend = rcParams['backend']
if backend in _interactive_bk:
figManager = _pylab_helpers.Gcf.get_active()
if figManager is not None:
canvas = figManager.canvas
if canvas.figure.stale:
canvas.draw()
show(block=False)
canvas.start_event_loop(interval)
return
# No on-screen figure is active, so sleep() is all we need.
import time
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 <http://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 is_string_like(num):
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(h)`` where *h* is a :class:`Figure` instance, closes that figure
``close(num)`` closes 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 is_string_like(arg):
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)
@docstring.copy_dedent(Figure.suptitle)
def suptitle(*args, **kwargs):
return gcf().suptitle(*args, **kwargs)
@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)
## 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 x-unit/y-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 positioned by the given grid definition.
Typical call signature::
subplot(nrows, ncols, plot_number)
Where *nrows* and *ncols* are used to notionally split the figure
into ``nrows * ncols`` sub-axes, and *plot_number* is used to identify
the particular subplot that this function is to create within the notional
grid. *plot_number* starts at 1, increments across rows first and has a
maximum of ``nrows * ncols``.
In the case when *nrows*, *ncols* and *plot_number* are all less than 10,
a convenience exists, such that the a 3 digit number can be given instead,
where the hundreds represent *nrows*, the tens represent *ncols* and the
units represent *plot_number*. For instance::
subplot(211)
produces a subaxes in a figure which represents the top plot (i.e. the
first) in a 2 row by 1 column notional grid (no grid actually exists,
but conceptually this is how the returned subplot has been positioned).
.. 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:`examples/pie_and_polar_charts/polar_scatter_demo.py`
For an example
**Example:**
.. plot:: mpl_examples/subplots_axes_and_figures/subplot_demo.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 : dict, optional
Dict with keywords passed to the :func:`figure` call. Note that all
keywords not recognized above will be automatically included here.
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 a subplot in a grid. The grid is specified by *shape*, at
location of *loc*, spanning *rowspan*, *colspan* cells in each
direction. The index for loc is 0-based. The current figure will
be used unless *fig* is specified. ::
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)
## 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)
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)
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)
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
The base colormaps are derived from those of the same name provided
with Matlab:
========= =======================================================
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
inferno perceptually uniform shades of black-red-yellow
jet a spectral map with dark endpoints, blue-cyan-yellow-red;
based on a fluid-jet simulation by NCSA [#]_
magma perceptually uniform shades of black-red-white
pink sequential increasing pastel black-pink-white, meant
for sepia tone colorization of photographs
plasma perceptually uniform shades of blue-red-yellow
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
viridis perceptually uniform shades of blue-green-yellow
winter linearly-increasing shades of blue-green
========= =======================================================
For the above list only, you can also set the colormap using the
corresponding pylab shortcut interface function, similar to Matlab::
imshow(X)
hot()
jet()
The next set of palettes are from the `Yorick scientific visualisation
package <http://dhmunro.github.io/yorick-doc/>`_, an evolution of
the GIST package, both by David H. Munro:
============ =======================================================
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
============ =======================================================
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
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
<http://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
<http://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 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 is_string_like(identifier):
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(left, height, width=0.8, bottom=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.bar(left, height, width=width, bottom=bottom, 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.barh)
def barh(bottom, width, height=0.8, left=None, 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.barh(bottom, width, height=height, left=left, **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, normed=False, weights=None, cumulative=False,
bottom=None, histtype='bar', align='mid', orientation='vertical',
rwidth=None, log=False, color=None, label=None, stacked=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.hist(x, bins=bins, range=range, normed=normed,
weights=weights, cumulative=cumulative, bottom=bottom,
histtype=histtype, align=align, orientation=orientation,
rwidth=rwidth, log=log, color=color, label=label,
stacked=stacked, 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