Source code for matplotlib.pyplot

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

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

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

    import numpy as np
    import matplotlib.pyplot as plt

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

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

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

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

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

import numpy as np

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

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

_log = logging.getLogger(__name__)


_code_objs = {
    cbook._rename_parameter:
        cbook._rename_parameter("", "old", "new", lambda new: None).__code__,
    cbook._make_keyword_only:
        cbook._make_keyword_only("", "p", lambda p: None).__code__,
}


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


## Global ##


_IP_REGISTERED = None
_INSTALL_FIG_OBSERVER = False


[docs]def install_repl_displayhook(): """ Install a repl display hook so that any stale figure are automatically redrawn when control is returned to the repl. This works both with IPython and with vanilla python shells. """ global _IP_REGISTERED global _INSTALL_FIG_OBSERVER class _NotIPython(Exception): pass # see if we have IPython hooks around, if use them try: if 'IPython' in sys.modules: from IPython import get_ipython ip = get_ipython() if ip is None: raise _NotIPython() if _IP_REGISTERED: return def post_execute(): if matplotlib.is_interactive(): draw_all() # IPython >= 2 try: ip.events.register('post_execute', post_execute) except AttributeError: # IPython 1.x ip.register_post_execute(post_execute) _IP_REGISTERED = post_execute _INSTALL_FIG_OBSERVER = False # trigger IPython's eventloop integration, if available from IPython.core.pylabtools import backend2gui ipython_gui_name = backend2gui.get(get_backend()) if ipython_gui_name: ip.enable_gui(ipython_gui_name) else: _INSTALL_FIG_OBSERVER = True # import failed or ipython is not running except (ImportError, _NotIPython): _INSTALL_FIG_OBSERVER = True
[docs]def uninstall_repl_displayhook(): """ Uninstall the matplotlib display hook. .. warning:: Need IPython >= 2 for this to work. For IPython < 2 will raise a ``NotImplementedError`` .. warning:: If you are using vanilla python and have installed another display hook this will reset ``sys.displayhook`` to what ever function was there when matplotlib installed it's displayhook, possibly discarding your changes. """ global _IP_REGISTERED global _INSTALL_FIG_OBSERVER if _IP_REGISTERED: from IPython import get_ipython ip = get_ipython() try: ip.events.unregister('post_execute', _IP_REGISTERED) except AttributeError as err: raise NotImplementedError("Can not unregister events " "in IPython < 2.0") from err _IP_REGISTERED = None if _INSTALL_FIG_OBSERVER: _INSTALL_FIG_OBSERVER = False
draw_all = _pylab_helpers.Gcf.draw_all @functools.wraps(matplotlib.set_loglevel) def set_loglevel(*args, **kwargs): # Ensure this appears in the pyplot docs. return matplotlib.set_loglevel(*args, **kwargs)
[docs]@_copy_docstring_and_deprecators(Artist.findobj) def findobj(o=None, match=None, include_self=True): if o is None: o = gcf() return o.findobj(match, include_self=include_self)
def _get_required_interactive_framework(backend_mod): return getattr( backend_mod.FigureCanvas, "required_interactive_framework", None)
[docs]def switch_backend(newbackend): """ Close all open figures and set the Matplotlib backend. The argument is case-insensitive. Switching to an interactive backend is possible only if no event loop for another interactive backend has started. Switching to and from non-interactive backends is always possible. Parameters ---------- newbackend : str The name of the backend to use. """ global _backend_mod close("all") if newbackend is rcsetup._auto_backend_sentinel: # Don't try to fallback on the cairo-based backends as they each have # an additional dependency (pycairo) over the agg-based backend, and # are of worse quality. for candidate in ["macosx", "qt5agg", "gtk3agg", "tkagg", "wxagg"]: try: switch_backend(candidate) except ImportError: continue else: rcParamsOrig['backend'] = candidate return else: # Switching to Agg should always succeed; if it doesn't, let the # exception propagate out. switch_backend("agg") rcParamsOrig["backend"] = "agg" return # Backends are implemented as modules, but "inherit" default method # implementations from backend_bases._Backend. This is achieved by # creating a "class" that inherits from backend_bases._Backend and whose # body is filled with the module's globals. backend_name = cbook._backend_module_name(newbackend) class backend_mod(matplotlib.backend_bases._Backend): locals().update(vars(importlib.import_module(backend_name))) required_framework = _get_required_interactive_framework(backend_mod) if required_framework is not None: current_framework = cbook._get_running_interactive_framework() if (current_framework and required_framework and current_framework != required_framework): raise ImportError( "Cannot load backend {!r} which requires the {!r} interactive " "framework, as {!r} is currently running".format( newbackend, required_framework, current_framework)) _log.debug("Loaded backend %s version %s.", newbackend, backend_mod.backend_version) rcParams['backend'] = rcParamsDefault['backend'] = newbackend _backend_mod = backend_mod for func_name in ["new_figure_manager", "draw_if_interactive", "show"]: globals()[func_name].__signature__ = inspect.signature( getattr(backend_mod, func_name)) # Need to keep a global reference to the backend for compatibility reasons. # See https://github.com/matplotlib/matplotlib/issues/6092 matplotlib.backends.backend = newbackend
def _warn_if_gui_out_of_main_thread(): if (_get_required_interactive_framework(_backend_mod) and threading.current_thread() is not threading.main_thread()): cbook._warn_external( "Starting a Matplotlib GUI outside of the main thread will likely " "fail.") # This function's signature is rewritten upon backend-load by switch_backend.
[docs]def new_figure_manager(*args, **kwargs): """Create a new figure manager instance.""" _warn_if_gui_out_of_main_thread() return _backend_mod.new_figure_manager(*args, **kwargs)
# This function's signature is rewritten upon backend-load by switch_backend.
[docs]def draw_if_interactive(*args, **kwargs): return _backend_mod.draw_if_interactive(*args, **kwargs)
# This function's signature is rewritten upon backend-load by switch_backend.
[docs]def show(*args, **kwargs): """ Display all open figures. In non-interactive mode, *block* defaults to True. All figures will display and show will not return until all windows are closed. If there are no figures, return immediately. In interactive mode *block* defaults to False. This will ensure that all of the figures are shown and this function immediately returns. Parameters ---------- block : bool, optional If `True` block and run the GUI main loop until all windows are closed. If `False` ensure that all windows are displayed and return immediately. In this case, you are responsible for ensuring that the event loop is running to have responsive figures. See Also -------- ion : enable interactive mode ioff : disable interactive mode """ _warn_if_gui_out_of_main_thread() return _backend_mod.show(*args, **kwargs)
[docs]def isinteractive(): """ Return if pyplot is in "interactive mode" or not. If in interactive mode then: - newly created figures will be shown immediately - figures will automatically redraw on change - `.pyplot.show` will not block by default If not in interactive mode then: - newly created figures and changes to figures will not be reflected until explicitly asked to be - `.pyplot.show` will block by default See Also -------- ion : enable interactive mode ioff : disable interactive mode show : show windows (and maybe block) pause : show windows, run GUI event loop, and block for a time """ return matplotlib.is_interactive()
[docs]def ioff(): """ Turn the interactive mode off. See Also -------- ion : enable interactive mode isinteractive : query current state show : show windows (and maybe block) pause : show windows, run GUI event loop, and block for a time """ matplotlib.interactive(False) uninstall_repl_displayhook()
[docs]def ion(): """ Turn the interactive mode on. See Also -------- ioff : disable interactive mode isinteractive : query current state show : show windows (and maybe block) pause : show windows, run GUI event loop, and block for a time """ matplotlib.interactive(True) install_repl_displayhook()
[docs]def pause(interval): """ Run the GUI event loop for *interval* seconds. If there is an active figure, it will be updated and displayed before the pause, and the GUI event loop (if any) will run during the pause. This can be used for crude animation. For more complex animation use :mod:`matplotlib.animation`. If there is no active figure, sleep for *interval* seconds instead. See Also -------- matplotlib.animation : Complex animation show : show figures and optional block forever """ manager = _pylab_helpers.Gcf.get_active() if manager is not None: canvas = manager.canvas if canvas.figure.stale: canvas.draw_idle() show(block=False) canvas.start_event_loop(interval) else: time.sleep(interval)
[docs]@_copy_docstring_and_deprecators(matplotlib.rc) def rc(group, **kwargs): matplotlib.rc(group, **kwargs)
[docs]@_copy_docstring_and_deprecators(matplotlib.rc_context) def rc_context(rc=None, fname=None): return matplotlib.rc_context(rc, fname)
[docs]@_copy_docstring_and_deprecators(matplotlib.rcdefaults) def rcdefaults(): matplotlib.rcdefaults() if matplotlib.is_interactive(): draw_all()
# getp/get/setp are explicitly reexported so that they show up in pyplot docs.
[docs]@_copy_docstring_and_deprecators(matplotlib.artist.getp) def getp(obj, *args, **kwargs): return matplotlib.artist.getp(obj, *args, **kwargs)
[docs]@_copy_docstring_and_deprecators(matplotlib.artist.get) def get(obj, *args, **kwargs): return matplotlib.artist.get(obj, *args, **kwargs)
[docs]@_copy_docstring_and_deprecators(matplotlib.artist.setp) def setp(obj, *args, **kwargs): return matplotlib.artist.setp(obj, *args, **kwargs)
[docs]def xkcd(scale=1, length=100, randomness=2): """ Turn on `xkcd <https://xkcd.com/>`_ sketch-style drawing mode. This will only have effect on things drawn after this function is called. For best results, the "Humor Sans" font should be installed: it is not included with Matplotlib. Parameters ---------- scale : float, optional The amplitude of the wiggle perpendicular to the source line. length : float, optional The length of the wiggle along the line. randomness : float, optional The scale factor by which the length is shrunken or expanded. Notes ----- This function works by a number of rcParams, so it will probably override others you have set before. If you want the effects of this function to be temporary, it can be used as a context manager, for example:: with plt.xkcd(): # This figure will be in XKCD-style fig1 = plt.figure() # ... # This figure will be in regular style fig2 = plt.figure() """ return _xkcd(scale, length, randomness)
class _xkcd: # This cannot be implemented in terms of rc_context() because this needs to # work as a non-contextmanager too. def __init__(self, scale, length, randomness): self._orig = rcParams.copy() if rcParams['text.usetex']: raise RuntimeError( "xkcd mode is not compatible with text.usetex = True") from matplotlib import patheffects rcParams.update({ 'font.family': ['xkcd', 'xkcd Script', 'Humor Sans', 'Comic Neue', 'Comic Sans MS'], 'font.size': 14.0, 'path.sketch': (scale, length, randomness), 'path.effects': [ patheffects.withStroke(linewidth=4, foreground="w")], 'axes.linewidth': 1.5, 'lines.linewidth': 2.0, 'figure.facecolor': 'white', 'grid.linewidth': 0.0, 'axes.grid': False, 'axes.unicode_minus': False, 'axes.edgecolor': 'black', 'xtick.major.size': 8, 'xtick.major.width': 3, 'ytick.major.size': 8, 'ytick.major.width': 3, }) def __enter__(self): return self def __exit__(self, *args): dict.update(rcParams, self._orig) ## Figures ##
[docs]def figure(num=None, # autoincrement if None, else integer from 1-N figsize=None, # defaults to rc figure.figsize dpi=None, # defaults to rc figure.dpi facecolor=None, # defaults to rc figure.facecolor edgecolor=None, # defaults to rc figure.edgecolor frameon=True, FigureClass=Figure, clear=False, **kwargs ): """ Create a new figure, or activate an existing figure. Parameters ---------- num : int or str, optional A unique identifier for the figure. If a figure with that identifier already exists, this figure is made active and returned. An integer refers to the ``Figure.number`` attribute, a string refers to the figure label. If there is no figure with the identifier or *num* is not given, a new figure is created, made active and returned. If *num* is an int, it will be used for the ``Figure.number`` attribute, otherwise, an auto-generated integer value is used (starting at 1 and incremented for each new figure). If *num* is a string, the figure label and the window title is set to this value. figsize : (float, float), default: :rc:`figure.figsize` Width, height in inches. dpi : float, default: :rc:`figure.dpi` The resolution of the figure in dots-per-inch. facecolor : color, default: :rc:`figure.facecolor` The background color. edgecolor : color, default: :rc:`figure.edgecolor` The border color. frameon : bool, default: True If False, suppress drawing the figure frame. FigureClass : subclass of `~matplotlib.figure.Figure` Optionally use a custom `.Figure` instance. clear : bool, default: False If True and the figure already exists, then it is cleared. tight_layout : bool or dict, default: :rc:`figure.autolayout` If ``False`` use *subplotpars*. If ``True`` adjust subplot parameters using `.tight_layout` with default padding. When providing a dict containing the keys ``pad``, ``w_pad``, ``h_pad``, and ``rect``, the default `.tight_layout` paddings will be overridden. constrained_layout : bool, default: :rc:`figure.constrained_layout.use` If ``True`` use constrained layout to adjust positioning of plot elements. Like ``tight_layout``, but designed to be more flexible. See :doc:`/tutorials/intermediate/constrainedlayout_guide` for examples. (Note: does not work with `add_subplot` or `~.pyplot.subplot2grid`.) **kwargs : optional See `~.matplotlib.figure.Figure` for other possible arguments. Returns ------- `~matplotlib.figure.Figure` The `.Figure` instance returned will also be passed to new_figure_manager in the backends, which allows to hook custom `.Figure` classes into the pyplot interface. Additional kwargs will be passed to the `.Figure` init function. Notes ----- If you are creating many figures, make sure you explicitly call `.pyplot.close` on the figures you are not using, because this will enable pyplot to properly clean up the memory. `~matplotlib.rcParams` defines the default values, which can be modified in the matplotlibrc file. """ if figsize is None: figsize = rcParams['figure.figsize'] if dpi is None: dpi = rcParams['figure.dpi'] if facecolor is None: facecolor = rcParams['figure.facecolor'] if edgecolor is None: edgecolor = rcParams['figure.edgecolor'] allnums = get_fignums() next_num = max(allnums) + 1 if allnums else 1 figLabel = '' if num is None: num = next_num elif isinstance(num, str): figLabel = num allLabels = get_figlabels() if figLabel not in allLabels: if figLabel == 'all': cbook._warn_external( "close('all') closes all existing figures") num = next_num else: inum = allLabels.index(figLabel) num = allnums[inum] else: num = int(num) # crude validation of num argument figManager = _pylab_helpers.Gcf.get_fig_manager(num) if figManager is None: max_open_warning = rcParams['figure.max_open_warning'] if len(allnums) == max_open_warning >= 1: cbook._warn_external( "More than %d figures have been opened. Figures " "created through the pyplot interface " "(`matplotlib.pyplot.figure`) are retained until " "explicitly closed and may consume too much memory. " "(To control this warning, see the rcParam " "`figure.max_open_warning`)." % max_open_warning, RuntimeWarning) if get_backend().lower() == 'ps': dpi = 72 figManager = new_figure_manager(num, figsize=figsize, dpi=dpi, facecolor=facecolor, edgecolor=edgecolor, frameon=frameon, FigureClass=FigureClass, **kwargs) fig = figManager.canvas.figure if figLabel: fig.set_label(figLabel) _pylab_helpers.Gcf._set_new_active_manager(figManager) # make sure backends (inline) that we don't ship that expect this # to be called in plotting commands to make the figure call show # still work. There is probably a better way to do this in the # FigureManager base class. draw_if_interactive() if _INSTALL_FIG_OBSERVER: fig.stale_callback = _auto_draw_if_interactive if clear: figManager.canvas.figure.clear() return figManager.canvas.figure
def _auto_draw_if_interactive(fig, val): """ An internal helper function for making sure that auto-redrawing works as intended in the plain python repl. Parameters ---------- fig : Figure A figure object which is assumed to be associated with a canvas """ if (val and matplotlib.is_interactive() and not fig.canvas.is_saving() and not fig.canvas._is_idle_drawing): # Some artists can mark themselves as stale in the middle of drawing # (e.g. axes position & tick labels being computed at draw time), but # this shouldn't trigger a redraw because the current redraw will # already take them into account. with fig.canvas._idle_draw_cntx(): fig.canvas.draw_idle()
[docs]def gcf(): """ Get the current figure. If no current figure exists, a new one is created using `~.pyplot.figure()`. """ figManager = _pylab_helpers.Gcf.get_active() if figManager is not None: return figManager.canvas.figure else: return figure()
[docs]def fignum_exists(num): """Return whether the figure with the given id exists.""" return _pylab_helpers.Gcf.has_fignum(num) or num in get_figlabels()
[docs]def get_fignums(): """Return a list of existing figure numbers.""" return sorted(_pylab_helpers.Gcf.figs)
[docs]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]
[docs]def get_current_fig_manager(): """ Return the figure manager of the current figure. The figure manager is a container for the actual backend-depended window that displays the figure on screen. If if no current figure exists, a new one is created an its figure manager is returned. Returns ------- `.FigureManagerBase` or backend-dependent subclass thereof """ return gcf().canvas.manager
[docs]@_copy_docstring_and_deprecators(FigureCanvasBase.mpl_connect) def connect(s, func): return gcf().canvas.mpl_connect(s, func)
[docs]@_copy_docstring_and_deprecators(FigureCanvasBase.mpl_disconnect) def disconnect(cid): return gcf().canvas.mpl_disconnect(cid)
[docs]def close(fig=None): """ Close a figure window. Parameters ---------- fig : None or int or str or `.Figure` The figure to close. There are a number of ways to specify this: - *None*: the current figure - `.Figure`: the given `.Figure` instance - ``int``: a figure number - ``str``: a figure name - 'all': all figures """ if fig is None: figManager = _pylab_helpers.Gcf.get_active() if figManager is None: return else: _pylab_helpers.Gcf.destroy(figManager) elif fig == 'all': _pylab_helpers.Gcf.destroy_all() elif isinstance(fig, int): _pylab_helpers.Gcf.destroy(fig) elif hasattr(fig, 'int'): # if we are dealing with a type UUID, we # can use its integer representation _pylab_helpers.Gcf.destroy(fig.int) elif isinstance(fig, str): allLabels = get_figlabels() if fig in allLabels: num = get_fignums()[allLabels.index(fig)] _pylab_helpers.Gcf.destroy(num) elif isinstance(fig, Figure): _pylab_helpers.Gcf.destroy_fig(fig) else: raise TypeError("close() argument must be a Figure, an int, a string, " "or None, not '%s'")
[docs]def clf(): """Clear the current figure.""" gcf().clf()
[docs]def draw(): """ Redraw the current figure. This is used to update a figure that has been altered, but not automatically re-drawn. If interactive mode is on (via `.ion()`), this should be only rarely needed, but there may be ways to modify the state of a figure without marking it as "stale". Please report these cases as bugs. This is equivalent to calling ``fig.canvas.draw_idle()``, where ``fig`` is the current figure. """ gcf().canvas.draw_idle()
[docs]@_copy_docstring_and_deprecators(Figure.savefig) def savefig(*args, **kwargs): fig = gcf() res = fig.savefig(*args, **kwargs) fig.canvas.draw_idle() # need this if 'transparent=True' to reset colors return res
## Putting things in figures ##
[docs]def figlegend(*args, **kwargs): return gcf().legend(*args, **kwargs)
if Figure.legend.__doc__: figlegend.__doc__ = Figure.legend.__doc__.replace("legend(", "figlegend(") ## Axes ##
[docs]@docstring.dedent_interpd def axes(arg=None, **kwargs): """ Add an axes to the current figure and make it the current axes. Call signatures:: plt.axes() plt.axes(rect, projection=None, polar=False, **kwargs) plt.axes(ax) Parameters ---------- arg : None or 4-tuple The exact behavior of this function depends on the type: - *None*: A new full window axes is added using ``subplot(111, **kwargs)``. - 4-tuple of floats *rect* = ``[left, bottom, width, height]``. A new axes is added with dimensions *rect* in normalized (0, 1) units using `~.Figure.add_axes` on the current figure. projection : {None, 'aitoff', 'hammer', 'lambert', 'mollweide', \ 'polar', 'rectilinear', str}, optional The projection type of the `~.axes.Axes`. *str* is the name of a custom projection, see `~matplotlib.projections`. The default None results in a 'rectilinear' projection. polar : bool, default: False If True, equivalent to projection='polar'. sharex, sharey : `~.axes.Axes`, optional Share the x or y `~matplotlib.axis` with sharex and/or sharey. The axis will have the same limits, ticks, and scale as the axis of the shared axes. label : str A label for the returned axes. Returns ------- `~.axes.Axes`, or a subclass of `~.axes.Axes` The returned axes class depends on the projection used. It is `~.axes.Axes` if rectilinear projection is used and `.projections.polar.PolarAxes` if polar projection is used. Other Parameters ---------------- **kwargs This method also takes the keyword arguments for the returned axes class. The keyword arguments for the rectilinear axes class `~.axes.Axes` can be found in the following table but there might also be other keyword arguments if another projection is used, see the actual axes class. %(Axes)s Notes ----- If the figure already has a axes with key (*args*, *kwargs*) then it will simply make that axes current and return it. This behavior is deprecated. Meanwhile, if you do not want this behavior (i.e., you want to force the creation of a new axes), you must use a unique set of args and kwargs. The axes *label* attribute has been exposed for this purpose: if you want two axes that are otherwise identical to be added to the figure, make sure you give them unique labels. See Also -------- .Figure.add_axes .pyplot.subplot .Figure.add_subplot .Figure.subplots .pyplot.subplots Examples -------- :: # Creating a new full window axes plt.axes() # Creating a new axes with specified dimensions and some kwargs plt.axes((left, bottom, width, height), facecolor='w') """ if arg is None: return subplot(111, **kwargs) else: return gcf().add_axes(arg, **kwargs)
[docs]def delaxes(ax=None): """ Remove an `~.axes.Axes` (defaulting to the current axes) from its figure. """ if ax is None: ax = gca() ax.remove()
[docs]def sca(ax): """ Set the current Axes to *ax* and the current Figure to the parent of *ax*. """ if not hasattr(ax.figure.canvas, "manager"): raise ValueError("Axes parent figure is not managed by pyplot") _pylab_helpers.Gcf.set_active(ax.figure.canvas.manager) ax.figure.sca(ax)
## More ways of creating axes ##
[docs]@docstring.dedent_interpd def subplot(*args, **kwargs): """ Add a subplot to the current figure. Wrapper of `.Figure.add_subplot` with a difference in behavior explained in the notes section. Call signatures:: subplot(nrows, ncols, index, **kwargs) subplot(pos, **kwargs) subplot(**kwargs) subplot(ax) Parameters ---------- *args : int, (int, int, *index*), or `.SubplotSpec`, default: (1, 1, 1) The position of the subplot described by one of - Three integers (*nrows*, *ncols*, *index*). The subplot will take the *index* position on a grid with *nrows* rows and *ncols* columns. *index* starts at 1 in the upper left corner and increases to the right. *index* can also be a two-tuple specifying the (*first*, *last*) indices (1-based, and including *last*) of the subplot, e.g., ``fig.add_subplot(3, 1, (1, 2))`` makes a subplot that spans the upper 2/3 of the figure. - A 3-digit integer. The digits are interpreted as if given separately as three single-digit integers, i.e. ``fig.add_subplot(235)`` is the same as ``fig.add_subplot(2, 3, 5)``. Note that this can only be used if there are no more than 9 subplots. - A `.SubplotSpec`. projection : {None, 'aitoff', 'hammer', 'lambert', 'mollweide', \ 'polar', 'rectilinear', str}, optional The projection type of the subplot (`~.axes.Axes`). *str* is the name of a custom projection, see `~matplotlib.projections`. The default None results in a 'rectilinear' projection. polar : bool, default: False If True, equivalent to projection='polar'. sharex, sharey : `~.axes.Axes`, optional Share the x or y `~matplotlib.axis` with sharex and/or sharey. The axis will have the same limits, ticks, and scale as the axis of the shared axes. label : str A label for the returned axes. Returns ------- `.axes.SubplotBase`, or another subclass of `~.axes.Axes` The axes of the subplot. The returned axes base class depends on the projection used. It is `~.axes.Axes` if rectilinear projection is used and `.projections.polar.PolarAxes` if polar projection is used. The returned axes is then a subplot subclass of the base class. Other Parameters ---------------- **kwargs This method also takes the keyword arguments for the returned axes base class; except for the *figure* argument. The keyword arguments for the rectilinear base class `~.axes.Axes` can be found in the following table but there might also be other keyword arguments if another projection is used. %(Axes)s Notes ----- Creating a subplot will delete any pre-existing subplot that overlaps with it beyond sharing a boundary:: import matplotlib.pyplot as plt # plot a line, implicitly creating a subplot(111) plt.plot([1, 2, 3]) # now create a subplot which represents the top plot of a grid # with 2 rows and 1 column. Since this subplot will overlap the # first, the plot (and its axes) previously created, will be removed plt.subplot(211) If you do not want this behavior, use the `.Figure.add_subplot` method or the `.pyplot.axes` function instead. If the figure already has a subplot with key (*args*, *kwargs*) then it will simply make that subplot current and return it. This behavior is deprecated. Meanwhile, if you do not want this behavior (i.e., you want to force the creation of a new subplot), you must use a unique set of args and kwargs. The axes *label* attribute has been exposed for this purpose: if you want two subplots that are otherwise identical to be added to the figure, make sure you give them unique labels. In rare circumstances, `.add_subplot` may be called with a single argument, a subplot axes instance already created in the present figure but not in the figure's list of axes. See Also -------- .Figure.add_subplot .pyplot.subplots .pyplot.axes .Figure.subplots Examples -------- :: plt.subplot(221) # equivalent but more general ax1=plt.subplot(2, 2, 1) # add a subplot with no frame ax2=plt.subplot(222, frameon=False) # add a polar subplot plt.subplot(223, projection='polar') # add a red subplot that shares the x-axis with ax1 plt.subplot(224, sharex=ax1, facecolor='red') # delete ax2 from the figure plt.delaxes(ax2) # add ax2 to the figure again plt.subplot(ax2) """ # if subplot called without arguments, create subplot(1, 1, 1) if len(args) == 0: args = (1, 1, 1) # This check was added because it is very easy to type # subplot(1, 2, False) when subplots(1, 2, False) was intended # (sharex=False, that is). In most cases, no error will # ever occur, but mysterious behavior can result because what was # intended to be the sharex argument is instead treated as a # subplot index for subplot() if len(args) >= 3 and isinstance(args[2], bool): cbook._warn_external("The subplot index argument to subplot() appears " "to be a boolean. Did you intend to use " "subplots()?") # Check for nrows and ncols, which are not valid subplot args: if 'nrows' in kwargs or 'ncols' in kwargs: raise TypeError("subplot() got an unexpected keyword argument 'ncols' " "and/or 'nrows'. Did you intend to call subplots()?") fig = gcf() ax = fig.add_subplot(*args, **kwargs) bbox = ax.bbox axes_to_delete = [] for other_ax in fig.axes: if other_ax == ax: continue if bbox.fully_overlaps(other_ax.bbox): axes_to_delete.append(other_ax) for ax_to_del in axes_to_delete: delaxes(ax_to_del) return ax
[docs]@cbook._make_keyword_only("3.3", "sharex") def subplots(nrows=1, ncols=1, sharex=False, sharey=False, squeeze=True, subplot_kw=None, gridspec_kw=None, **fig_kw): """ Create a figure and a set of subplots. This utility wrapper makes it convenient to create common layouts of subplots, including the enclosing figure object, in a single call. Parameters ---------- nrows, ncols : int, default: 1 Number of rows/columns of the subplot grid. sharex, sharey : bool or {'none', 'all', 'row', 'col'}, default: False Controls sharing of properties among x (*sharex*) or y (*sharey*) axes: - True or 'all': x- or y-axis will be shared among all subplots. - False or 'none': each subplot x- or y-axis will be independent. - 'row': each subplot row will share an x- or y-axis. - 'col': each subplot column will share an x- or y-axis. When subplots have a shared x-axis along a column, only the x tick labels of the bottom subplot are created. Similarly, when subplots have a shared y-axis along a row, only the y tick labels of the first column subplot are created. To later turn other subplots' ticklabels on, use `~matplotlib.axes.Axes.tick_params`. squeeze : bool, default: True - If True, extra dimensions are squeezed out from the returned array of `~matplotlib.axes.Axes`: - if only one subplot is constructed (nrows=ncols=1), the resulting single Axes object is returned as a scalar. - for Nx1 or 1xM subplots, the returned object is a 1D numpy object array of Axes objects. - for NxM, subplots with N>1 and M>1 are returned as a 2D array. - If False, no squeezing at all is done: the returned Axes object is always a 2D array containing Axes instances, even if it ends up being 1x1. subplot_kw : dict, optional Dict with keywords passed to the `~matplotlib.figure.Figure.add_subplot` call used to create each subplot. gridspec_kw : dict, optional Dict with keywords passed to the `~matplotlib.gridspec.GridSpec` constructor used to create the grid the subplots are placed on. **fig_kw All additional keyword arguments are passed to the `.pyplot.figure` call. Returns ------- fig : `~.figure.Figure` ax : `.axes.Axes` or array of Axes *ax* can be either a single `~matplotlib.axes.Axes` object or an array of Axes objects if more than one subplot was created. The dimensions of the resulting array can be controlled with the squeeze keyword, see above. Typical idioms for handling the return value are:: # using the variable ax for single a Axes fig, ax = plt.subplots() # using the variable axs for multiple Axes fig, axs = plt.subplots(2, 2) # using tuple unpacking for multiple Axes fig, (ax1, ax2) = plt.subplot(1, 2) fig, ((ax1, ax2), (ax3, ax4)) = plt.subplot(2, 2) The names ``ax`` and pluralized ``axs`` are preferred over ``axes`` because for the latter it's not clear if it refers to a single `~.axes.Axes` instance or a collection of these. See Also -------- .pyplot.figure .pyplot.subplot .pyplot.axes .Figure.subplots .Figure.add_subplot Examples -------- :: # First create some toy data: x = np.linspace(0, 2*np.pi, 400) y = np.sin(x**2) # Create just a figure and only one subplot fig, ax = plt.subplots() ax.plot(x, y) ax.set_title('Simple plot') # Create two subplots and unpack the output array immediately f, (ax1, ax2) = plt.subplots(1, 2, sharey=True) ax1.plot(x, y) ax1.set_title('Sharing Y axis') ax2.scatter(x, y) # Create four polar axes and access them through the returned array fig, axs = plt.subplots(2, 2, subplot_kw=dict(polar=True)) axs[0, 0].plot(x, y) axs[1, 1].scatter(x, y) # Share a X axis with each column of subplots plt.subplots(2, 2, sharex='col') # Share a Y axis with each row of subplots plt.subplots(2, 2, sharey='row') # Share both X and Y axes with all subplots plt.subplots(2, 2, sharex='all', sharey='all') # Note that this is the same as plt.subplots(2, 2, sharex=True, sharey=True) # Create figure number 10 with a single subplot # and clears it if it already exists. fig, ax = plt.subplots(num=10, clear=True) """ 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
[docs]def subplot_mosaic(layout, *, subplot_kw=None, gridspec_kw=None, empty_sentinel='.', **fig_kw): """ Build a layout of Axes based on ASCII art or nested lists. This is a helper function to build complex GridSpec layouts visually. .. note :: This API is provisional and may be revised in the future based on early user feedback. Parameters ---------- layout : list of list of {hashable or nested} or str A visual layout of how you want your Axes to be arranged labeled as strings. For example :: x = [['A panel', 'A panel', 'edge'], ['C panel', '.', 'edge']] Produces 4 axes: - 'A panel' which is 1 row high and spans the first two columns - 'edge' which is 2 rows high and is on the right edge - 'C panel' which in 1 row and 1 column wide in the bottom left - a blank space 1 row and 1 column wide in the bottom center Any of the entries in the layout can be a list of lists of the same form to create nested layouts. If input is a str, then it must be of the form :: ''' AAE C.E ''' where each character is a column and each line is a row. This only allows only single character Axes labels and does not allow nesting but is very terse. subplot_kw : dict, optional Dictionary with keywords passed to the `.Figure.add_subplot` call used to create each subplot. gridspec_kw : dict, optional Dictionary with keywords passed to the `.GridSpec` constructor used to create the grid the subplots are placed on. empty_sentinel : object, optional Entry in the layout to mean "leave this space empty". Defaults to ``'.'``. Note, if *layout* is a string, it is processed via `inspect.cleandoc` to remove leading white space, which may interfere with using white-space as the empty sentinel. **fig_kw All additional keyword arguments are passed to the `.pyplot.figure` call. Returns ------- fig : `~.figure.Figure` The new figure dict[label, Axes] A dictionary mapping the labels to the Axes objects. """ fig = figure(**fig_kw) ax_dict = fig.subplot_mosaic( layout, subplot_kw=subplot_kw, gridspec_kw=gridspec_kw, empty_sentinel=empty_sentinel ) return fig, ax_dict
[docs]def subplot2grid(shape, loc, rowspan=1, colspan=1, fig=None, **kwargs): """ Create a subplot at a specific location inside a regular grid. Parameters ---------- shape : (int, int) Number of rows and of columns of the grid in which to place axis. loc : (int, int) Row number and column number of the axis location within the grid. rowspan : int, default: 1 Number of rows for the axis to span downwards. colspan : int, default: 1 Number of columns for the axis to span to the right. fig : `.Figure`, optional Figure to place the subplot in. Defaults to the current figure. **kwargs Additional keyword arguments are handed to `~.Figure.add_subplot`. Returns ------- `.axes.SubplotBase`, or another subclass of `~.axes.Axes` The axes of the subplot. The returned axes base class depends on the projection used. It is `~.axes.Axes` if rectilinear projection is used and `.projections.polar.PolarAxes` if polar projection is used. The returned axes is then a subplot subclass of the base class. Notes ----- The following call :: ax = subplot2grid((nrows, ncols), (row, col), rowspan, colspan) is identical to :: fig = gcf() gs = fig.add_gridspec(nrows, ncols) ax = fig.add_subplot(gs[row:row+rowspan, col:col+colspan]) """ if fig is None: fig = gcf() rows, cols = shape gs = GridSpec._check_gridspec_exists(fig, rows, cols) subplotspec = gs.new_subplotspec(loc, rowspan=rowspan, colspan=colspan) ax = fig.add_subplot(subplotspec, **kwargs) bbox = ax.bbox axes_to_delete = [] for other_ax in fig.axes: if other_ax == ax: continue if bbox.fully_overlaps(other_ax.bbox): axes_to_delete.append(other_ax) for ax_to_del in axes_to_delete: delaxes(ax_to_del) return ax
[docs]def twinx(ax=None): """ Make and return a second axes that shares the *x*-axis. The new axes will overlay *ax* (or the current axes if *ax* is *None*), and its ticks will be on the right. Examples -------- :doc:`/gallery/subplots_axes_and_figures/two_scales` """ if ax is None: ax = gca() ax1 = ax.twinx() return ax1
[docs]def twiny(ax=None): """ Make and return a second axes that shares the *y*-axis. The new axes will overlay *ax* (or the current axes if *ax* is *None*), and its ticks will be on the top. Examples -------- :doc:`/gallery/subplots_axes_and_figures/two_scales` """ if ax is None: ax = gca() ax1 = ax.twiny() return ax1
[docs]def subplot_tool(targetfig=None): """ Launch a subplot tool window for a figure. A :class:`matplotlib.widgets.SubplotTool` instance is returned. """ if targetfig is None: targetfig = gcf() with rc_context({'toolbar': 'None'}): # No nav toolbar for the toolfig. toolfig = figure(figsize=(6, 3)) toolfig.subplots_adjust(top=0.9) if hasattr(targetfig.canvas, "manager"): # Restore the current figure. _pylab_helpers.Gcf.set_active(targetfig.canvas.manager) return SubplotTool(targetfig, toolfig)
# After deprecation elapses, this can be autogenerated by boilerplate.py.
[docs]@cbook._make_keyword_only("3.3", "pad") def tight_layout(pad=1.08, h_pad=None, w_pad=None, rect=None): """ Adjust the padding between and around subplots. Parameters ---------- pad : float, default: 1.08 Padding between the figure edge and the edges of subplots, as a fraction of the font size. h_pad, w_pad : float, default: *pad* Padding (height/width) between edges of adjacent subplots, as a fraction of the font size. rect : tuple (left, bottom, right, top), default: (0, 0, 1, 1) A rectangle in normalized figure coordinates into which the whole subplots area (including labels) will fit. """ gcf().tight_layout(pad=pad, h_pad=h_pad, w_pad=w_pad, rect=rect)
[docs]def box(on=None): """ Turn the axes box on or off on the current axes. Parameters ---------- on : bool or None The new `~matplotlib.axes.Axes` box state. If ``None``, toggle the state. See Also -------- :meth:`matplotlib.axes.Axes.set_frame_on` :meth:`matplotlib.axes.Axes.get_frame_on` """ ax = gca() if on is None: on = not ax.get_frame_on() ax.set_frame_on(on)
## Axis ##
[docs]def xlim(*args, **kwargs): """ Get or set the x limits of the current axes. Call signatures:: left, right = xlim() # return the current xlim xlim((left, right)) # set the xlim to left, right xlim(left, right) # set the xlim to left, right If you do not specify args, you can pass *left* or *right* as kwargs, i.e.:: xlim(right=3) # adjust the right leaving left unchanged xlim(left=1) # adjust the left leaving right unchanged Setting limits turns autoscaling off for the x-axis. Returns ------- left, right A tuple of the new x-axis limits. Notes ----- Calling this function with no arguments (e.g. ``xlim()``) is the pyplot equivalent of calling `~.Axes.get_xlim` on the current axes. Calling this function with arguments is the pyplot equivalent of calling `~.Axes.set_xlim` on the current axes. All arguments are passed though. """ ax = gca() if not args and not kwargs: return ax.get_xlim() ret = ax.set_xlim(*args, **kwargs) return ret
[docs]def ylim(*args, **kwargs): """ Get or set the y-limits of the current axes. Call signatures:: bottom, top = ylim() # return the current ylim ylim((bottom, top)) # set the ylim to bottom, top ylim(bottom, top) # set the ylim to bottom, top If you do not specify args, you can alternatively pass *bottom* or *top* as kwargs, i.e.:: ylim(top=3) # adjust the top leaving bottom unchanged ylim(bottom=1) # adjust the bottom leaving top unchanged Setting limits turns autoscaling off for the y-axis. Returns ------- bottom, top A tuple of the new y-axis limits. Notes ----- Calling this function with no arguments (e.g. ``ylim()``) is the pyplot equivalent of calling `~.Axes.get_ylim` on the current axes. Calling this function with arguments is the pyplot equivalent of calling `~.Axes.set_ylim` on the current axes. All arguments are passed though. """ ax = gca() if not args and not kwargs: return ax.get_ylim() ret = ax.set_ylim(*args, **kwargs) return ret
[docs]def xticks(ticks=None, labels=None, **kwargs): """ Get or set the current tick locations and labels of the x-axis. Pass no arguments to return the current values without modifying them. Parameters ---------- ticks : array-like, optional The list of xtick locations. Passing an empty list removes all xticks. labels : array-like, optional The labels to place at the given *ticks* locations. This argument can only be passed if *ticks* is passed as well. **kwargs `.Text` properties can be used to control the appearance of the labels. Returns ------- locs The list of xtick locations. labels The list of xlabel `.Text` objects. Notes ----- Calling this function with no arguments (e.g. ``xticks()``) is the pyplot equivalent of calling `~.Axes.get_xticks` and `~.Axes.get_xticklabels` on the current axes. Calling this function with arguments is the pyplot equivalent of calling `~.Axes.set_xticks` and `~.Axes.set_xticklabels` on the current axes. Examples -------- >>> locs, labels = xticks() # Get the current locations and labels. >>> xticks(np.arange(0, 1, step=0.2)) # Set label locations. >>> xticks(np.arange(3), ['Tom', 'Dick', 'Sue']) # Set text labels. >>> xticks([0, 1, 2], ['January', 'February', 'March'], ... rotation=20) # Set text labels and properties. >>> xticks([]) # Disable xticks. """ ax = gca() if ticks is None: locs = ax.get_xticks() if labels is not None: raise TypeError("xticks(): Parameter 'labels' can't be set " "without setting 'ticks'") else: locs = ax.set_xticks(ticks) if labels is None: labels = ax.get_xticklabels() else: labels = ax.set_xticklabels(labels, **kwargs) for l in labels: l.update(kwargs) return locs, labels
[docs]def yticks(ticks=None, labels=None, **kwargs): """ Get or set the current tick locations and labels of the y-axis. Pass no arguments to return the current values without modifying them. Parameters ---------- ticks : array-like, optional The list of ytick locations. Passing an empty list removes all yticks. labels : array-like, optional The labels to place at the given *ticks* locations. This argument can only be passed if *ticks* is passed as well. **kwargs `.Text` properties can be used to control the appearance of the labels. Returns ------- locs The list of ytick locations. labels The list of ylabel `.Text` objects. Notes ----- Calling this function with no arguments (e.g. ``yticks()``) is the pyplot equivalent of calling `~.Axes.get_yticks` and `~.Axes.get_yticklabels` on the current axes. Calling this function with arguments is the pyplot equivalent of calling `~.Axes.set_yticks` and `~.Axes.set_yticklabels` on the current axes. Examples -------- >>> locs, labels = yticks() # Get the current locations and labels. >>> yticks(np.arange(0, 1, step=0.2)) # Set label locations. >>> yticks(np.arange(3), ['Tom', 'Dick', 'Sue']) # Set text labels. >>> yticks([0, 1, 2], ['January', 'February', 'March'], ... rotation=45) # Set text labels and properties. >>> yticks([]) # Disable yticks. """ ax = gca() if ticks is None: locs = ax.get_yticks() if labels is not None: raise TypeError("yticks(): Parameter 'labels' can't be set " "without setting 'ticks'") else: locs = ax.set_yticks(ticks) if labels is None: labels = ax.get_yticklabels() else: labels = ax.set_yticklabels(labels, **kwargs) for l in labels: l.update(kwargs) return locs, labels
[docs]def rgrids(radii=None, labels=None, angle=None, fmt=None, **kwargs): """ Get or set the radial gridlines on the current polar plot. Call signatures:: lines, labels = rgrids() lines, labels = rgrids(radii, labels=None, angle=22.5, fmt=None, **kwargs) When called with no arguments, `.rgrids` simply returns the tuple (*lines*, *labels*). When called with arguments, the labels will appear at the specified radial distances and angle. Parameters ---------- radii : tuple with floats The radii for the radial gridlines labels : tuple with strings or None The labels to use at each radial gridline. The `matplotlib.ticker.ScalarFormatter` will be used if None. angle : float The angular position of the radius labels in degrees. fmt : str or None Format string used in `matplotlib.ticker.FormatStrFormatter`. For example '%f'. Returns ------- lines : list of `.lines.Line2D` The radial gridlines. labels : list of `.text.Text` The tick labels. Other Parameters ---------------- **kwargs *kwargs* are optional `~.Text` properties for the labels. See Also -------- .pyplot.thetagrids .projections.polar.PolarAxes.set_rgrids .Axis.get_gridlines .Axis.get_ticklabels Examples -------- :: # set the locations of the radial gridlines lines, labels = rgrids( (0.25, 0.5, 1.0) ) # set the locations and labels of the radial gridlines lines, labels = rgrids( (0.25, 0.5, 1.0), ('Tom', 'Dick', 'Harry' )) """ ax = gca() if not isinstance(ax, PolarAxes): raise RuntimeError('rgrids only defined for polar axes') if all(p is None for p in [radii, labels, angle, fmt]) and not kwargs: lines = ax.yaxis.get_gridlines() labels = ax.yaxis.get_ticklabels() else: lines, labels = ax.set_rgrids( radii, labels=labels, angle=angle, fmt=fmt, **kwargs) return lines, labels
[docs]def thetagrids(angles=None, labels=None, fmt=None, **kwargs): """ Get or set the theta gridlines on the current polar plot. Call signatures:: lines, labels = thetagrids() lines, labels = thetagrids(angles, labels=None, fmt=None, **kwargs) When called with no arguments, `.thetagrids` simply returns the tuple (*lines*, *labels*). When called with arguments, the labels will appear at the specified angles. Parameters ---------- angles : tuple with floats, degrees The angles of the theta gridlines. labels : tuple with strings or None The labels to use at each radial gridline. The `.projections.polar.ThetaFormatter` will be used if None. fmt : str or None Format string used in `matplotlib.ticker.FormatStrFormatter`. For example '%f'. Note that the angle in radians will be used. Returns ------- lines : list of `.lines.Line2D` The theta gridlines. labels : list of `.text.Text` The tick labels. Other Parameters ---------------- **kwargs *kwargs* are optional `~.Text` properties for the labels. See Also -------- .pyplot.rgrids .projections.polar.PolarAxes.set_thetagrids .Axis.get_gridlines .Axis.get_ticklabels Examples -------- :: # set the locations of the angular gridlines lines, labels = thetagrids(range(45, 360, 90)) # set the locations and labels of the angular gridlines lines, labels = thetagrids(range(45, 360, 90), ('NE', 'NW', 'SW', 'SE')) """ ax = gca() if not isinstance(ax, PolarAxes): raise RuntimeError('thetagrids only defined for polar axes') if all(param is None for param in [angles, labels, fmt]) and not kwargs: lines = ax.xaxis.get_ticklines() labels = ax.xaxis.get_ticklabels() else: lines, labels = ax.set_thetagrids(angles, labels=labels, fmt=fmt, **kwargs) return lines, labels
## Plotting Info ##
[docs]def plotting(): pass
[docs]def get_plot_commands(): """ Get a sorted list of all of the plotting commands. """ # This works by searching for all functions in this module and removing # a few hard-coded exclusions, as well as all of the colormap-setting # functions, and anything marked as private with a preceding underscore. exclude = {'colormaps', 'colors', 'connect', 'disconnect', 'get_plot_commands', 'get_current_fig_manager', 'ginput', 'plotting', 'waitforbuttonpress'} exclude |= set(colormaps()) this_module = inspect.getmodule(get_plot_commands) return sorted( name for name, obj in globals().items() if not name.startswith('_') and name not in exclude and inspect.isfunction(obj) and inspect.getmodule(obj) is this_module)
[docs]def colormaps(): """ Matplotlib provides a number of colormaps, and others can be added using :func:`~matplotlib.cm.register_cmap`. This function documents the built-in colormaps, and will also return a list of all registered colormaps if called. You can set the colormap for an image, pcolor, scatter, etc, using a keyword argument:: imshow(X, cmap=cm.hot) or using the :func:`set_cmap` function:: imshow(X) pyplot.set_cmap('hot') pyplot.set_cmap('jet') In interactive mode, :func:`set_cmap` will update the colormap post-hoc, allowing you to see which one works best for your data. All built-in colormaps can be reversed by appending ``_r``: For instance, ``gray_r`` is the reverse of ``gray``. There are several common color schemes used in visualization: Sequential schemes for unipolar data that progresses from low to high Diverging schemes for bipolar data that emphasizes positive or negative deviations from a central value Cyclic schemes for plotting values that wrap around at the endpoints, such as phase angle, wind direction, or time of day Qualitative schemes for nominal data that has no inherent ordering, where color is used only to distinguish categories Matplotlib ships with 4 perceptually uniform color maps which are the recommended color maps for sequential data: ========= =================================================== Colormap Description ========= =================================================== inferno perceptually uniform shades of black-red-yellow magma perceptually uniform shades of black-red-white plasma perceptually uniform shades of blue-red-yellow viridis perceptually uniform shades of blue-green-yellow ========= =================================================== The following colormaps are based on the `ColorBrewer <https://colorbrewer2.org>`_ color specifications and designs developed by Cynthia Brewer: ColorBrewer Diverging (luminance is highest at the midpoint, and decreases towards differently-colored endpoints): ======== =================================== Colormap Description ======== =================================== BrBG brown, white, blue-green PiYG pink, white, yellow-green PRGn purple, white, green PuOr orange, white, purple RdBu red, white, blue RdGy red, white, gray RdYlBu red, yellow, blue RdYlGn red, yellow, green Spectral red, orange, yellow, green, blue ======== =================================== ColorBrewer Sequential (luminance decreases monotonically): ======== ==================================== Colormap Description ======== ==================================== Blues white to dark blue BuGn white, light blue, dark green BuPu white, light blue, dark purple GnBu white, light green, dark blue Greens white to dark green Greys white to black (not linear) Oranges white, orange, dark brown OrRd white, orange, dark red PuBu white, light purple, dark blue PuBuGn white, light purple, dark green PuRd white, light purple, dark red Purples white to dark purple RdPu white, pink, dark purple Reds white to dark red YlGn light yellow, dark green YlGnBu light yellow, light green, dark blue YlOrBr light yellow, orange, dark brown YlOrRd light yellow, orange, dark red ======== ==================================== ColorBrewer Qualitative: (For plotting nominal data, `.ListedColormap` is used, not `.LinearSegmentedColormap`. Different sets of colors are recommended for different numbers of categories.) * Accent * Dark2 * Paired * Pastel1 * Pastel2 * Set1 * Set2 * Set3 A set of colormaps derived from those of the same name provided with Matlab are also included: ========= ======================================================= Colormap Description ========= ======================================================= autumn sequential linearly-increasing shades of red-orange-yellow bone sequential increasing black-white color map with a tinge of blue, to emulate X-ray film cool linearly-decreasing shades of cyan-magenta copper sequential increasing shades of black-copper flag repetitive red-white-blue-black pattern (not cyclic at endpoints) gray sequential linearly-increasing black-to-white grayscale hot sequential black-red-yellow-white, to emulate blackbody radiation from an object at increasing temperatures jet a spectral map with dark endpoints, blue-cyan-yellow-red; based on a fluid-jet simulation by NCSA [#]_ pink sequential increasing pastel black-pink-white, meant for sepia tone colorization of photographs prism repetitive red-yellow-green-blue-purple-...-green pattern (not cyclic at endpoints) spring linearly-increasing shades of magenta-yellow summer sequential linearly-increasing shades of green-yellow winter linearly-increasing shades of blue-green ========= ======================================================= A set of palettes from the `Yorick scientific visualisation package <https://dhmunro.github.io/yorick-doc/>`_, an evolution of the GIST package, both by David H. Munro are included: ============ ======================================================= Colormap Description ============ ======================================================= gist_earth mapmaker's colors from dark blue deep ocean to green lowlands to brown highlands to white mountains gist_heat sequential increasing black-red-orange-white, to emulate blackbody radiation from an iron bar as it grows hotter gist_ncar pseudo-spectral black-blue-green-yellow-red-purple-white colormap from National Center for Atmospheric Research [#]_ gist_rainbow runs through the colors in spectral order from red to violet at full saturation (like *hsv* but not cyclic) gist_stern "Stern special" color table from Interactive Data Language software ============ ======================================================= A set of cyclic color maps: ================ ================================================= Colormap Description ================ ================================================= hsv red-yellow-green-cyan-blue-magenta-red, formed by changing the hue component in the HSV color space twilight perceptually uniform shades of white-blue-black-red-white twilight_shifted perceptually uniform shades of black-blue-white-red-black ================ ================================================= Other miscellaneous schemes: ============= ======================================================= Colormap Description ============= ======================================================= afmhot sequential black-orange-yellow-white blackbody spectrum, commonly used in atomic force microscopy brg blue-red-green bwr diverging blue-white-red coolwarm diverging blue-gray-red, meant to avoid issues with 3D shading, color blindness, and ordering of colors [#]_ CMRmap "Default colormaps on color images often reproduce to confusing grayscale images. The proposed colormap maintains an aesthetically pleasing color image that automatically reproduces to a monotonic grayscale with discrete, quantifiable saturation levels." [#]_ cubehelix Unlike most other color schemes cubehelix was designed by D.A. Green to be monotonically increasing in terms of perceived brightness. Also, when printed on a black and white postscript printer, the scheme results in a greyscale with monotonically increasing brightness. This color scheme is named cubehelix because the (r, g, b) values produced can be visualised as a squashed helix around the diagonal in the (r, g, b) color cube. gnuplot gnuplot's traditional pm3d scheme (black-blue-red-yellow) gnuplot2 sequential color printable as gray (black-blue-violet-yellow-white) ocean green-blue-white rainbow spectral purple-blue-green-yellow-orange-red colormap with diverging luminance seismic diverging blue-white-red nipy_spectral black-purple-blue-green-yellow-red-white spectrum, originally from the Neuroimaging in Python project terrain mapmaker's colors, blue-green-yellow-brown-white, originally from IGOR Pro turbo Spectral map (purple-blue-green-yellow-orange-red) with a bright center and darker endpoints. A smoother alternative to jet. ============= ======================================================= The following colormaps are redundant and may be removed in future versions. It's recommended to use the names in the descriptions instead, which produce identical output: ========= ======================================================= Colormap Description ========= ======================================================= gist_gray identical to *gray* gist_yarg identical to *gray_r* binary identical to *gray_r* ========= ======================================================= .. rubric:: Footnotes .. [#] Rainbow colormaps, ``jet`` in particular, are considered a poor choice for scientific visualization by many researchers: `Rainbow Color Map (Still) Considered Harmful <https://ieeexplore.ieee.org/document/4118486/?arnumber=4118486>`_ .. [#] Resembles "BkBlAqGrYeOrReViWh200" from NCAR Command Language. See `Color Table Gallery <https://www.ncl.ucar.edu/Document/Graphics/color_table_gallery.shtml>`_ .. [#] See `Diverging Color Maps for Scientific Visualization <http://www.kennethmoreland.com/color-maps/>`_ by Kenneth Moreland. .. [#] See `A Color Map for Effective Black-and-White Rendering of Color-Scale Images <https://www.mathworks.com/matlabcentral/fileexchange/2662-cmrmap-m>`_ by Carey Rappaport """ return sorted(cm._cmap_registry)
def _setup_pyplot_info_docstrings(): """ Setup the docstring of `plotting` and of the colormap-setting functions. These must be done after the entire module is imported, so it is called from the end of this module, which is generated by boilerplate.py. """ commands = get_plot_commands() first_sentence = re.compile(r"(?:\s*).+?\.(?:\s+|$)", flags=re.DOTALL) # Collect the first sentence of the docstring for all of the # plotting commands. rows = [] max_name = len("Function") max_summary = len("Description") for name in commands: doc = globals()[name].__doc__ summary = '' if doc is not None: match = first_sentence.match(doc) if match is not None: summary = inspect.cleandoc(match.group(0)).replace('\n', ' ') name = '`%s`' % name rows.append([name, summary]) max_name = max(max_name, len(name)) max_summary = max(max_summary, len(summary)) separator = '=' * max_name + ' ' + '=' * max_summary lines = [ separator, '{:{}} {:{}}'.format('Function', max_name, 'Description', max_summary), separator, ] + [ '{:{}} {:{}}'.format(name, max_name, summary, max_summary) for name, summary in rows ] + [ separator, ] plotting.__doc__ = '\n'.join(lines) for cm_name in colormaps(): if cm_name in globals(): globals()[cm_name].__doc__ = f""" Set the colormap to {cm_name!r}. This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ ## Plotting part 1: manually generated functions and wrappers ##
[docs]@_copy_docstring_and_deprecators(Figure.colorbar) def colorbar(mappable=None, cax=None, ax=None, **kw): if mappable is None: mappable = gci() if mappable is None: raise RuntimeError('No mappable was found to use for colorbar ' 'creation. First define a mappable such as ' 'an image (with imshow) or a contour set (' 'with contourf).') if ax is None: ax = gca() ret = gcf().colorbar(mappable, cax=cax, ax=ax, **kw) return ret
[docs]def clim(vmin=None, vmax=None): """ Set the color limits of the current image. If either *vmin* or *vmax* is None, the image min/max respectively will be used for color scaling. If you want to set the clim of multiple images, use `~.ScalarMappable.set_clim` on every image, for example:: for im in gca().get_images(): im.set_clim(0, 0.5) """ im = gci() if im is None: raise RuntimeError('You must first define an image, e.g., with imshow') im.set_clim(vmin, vmax)
[docs]def set_cmap(cmap): """ Set the default colormap, and applies it to the current image if any. Parameters ---------- cmap : `~matplotlib.colors.Colormap` or str A colormap instance or the name of a registered colormap. See Also -------- colormaps matplotlib.cm.register_cmap matplotlib.cm.get_cmap """ cmap = cm.get_cmap(cmap) rc('image', cmap=cmap.name) im = gci() if im is not None: im.set_cmap(cmap)
[docs]@_copy_docstring_and_deprecators(matplotlib.image.imread) def imread(fname, format=None): return matplotlib.image.imread(fname, format)
[docs]@_copy_docstring_and_deprecators(matplotlib.image.imsave) def imsave(fname, arr, **kwargs): return matplotlib.image.imsave(fname, arr, **kwargs)
[docs]def matshow(A, fignum=None, **kwargs): """ Display an array as a matrix in a new figure window. The origin is set at the upper left hand corner and rows (first dimension of the array) are displayed horizontally. The aspect ratio of the figure window is that of the array, unless this would make an excessively short or narrow figure. Tick labels for the xaxis are placed on top. Parameters ---------- A : array-like(M, N) The matrix to be displayed. fignum : None or int or False If *None*, create a new figure window with automatic numbering. If a nonzero integer, draw into the figure with the given number (create it if it does not exist). If 0, use the current axes (or create one if it does not exist). .. note:: Because of how `.Axes.matshow` tries to set the figure aspect ratio to be the one of the array, strange things may happen if you reuse an existing figure. Returns ------- `~matplotlib.image.AxesImage` Other Parameters ---------------- **kwargs : `~matplotlib.axes.Axes.imshow` arguments """ A = np.asanyarray(A) if fignum == 0: ax = gca() else: # Extract actual aspect ratio of array and make appropriately sized # figure. fig = figure(fignum, figsize=figaspect(A)) ax = fig.add_axes([0.15, 0.09, 0.775, 0.775]) im = ax.matshow(A, **kwargs) sci(im) return im
[docs]def polar(*args, **kwargs): """ Make a polar plot. call signature:: polar(theta, r, **kwargs) Multiple *theta*, *r* arguments are supported, with format strings, as in `plot`. """ # If an axis already exists, check if it has a polar projection if gcf().get_axes(): if not isinstance(gca(), PolarAxes): cbook._warn_external('Trying to create polar plot on an axis ' 'that does not have a polar projection.') ax = gca(polar=True) ret = ax.plot(*args, **kwargs) return ret
# If rcParams['backend_fallback'] is true, and an interactive backend is # requested, ignore rcParams['backend'] and force selection of a backend that # is compatible with the current running interactive framework. if (rcParams["backend_fallback"] and dict.__getitem__(rcParams, "backend") in ( set(_interactive_bk) - {'WebAgg', 'nbAgg'}) and cbook._get_running_interactive_framework()): dict.__setitem__(rcParams, "backend", rcsetup._auto_backend_sentinel) # Set up the backend. switch_backend(rcParams["backend"]) # Just to be safe. Interactive mode can be turned on without # calling `plt.ion()` so register it again here. # This is safe because multiple calls to `install_repl_displayhook` # are no-ops and the registered function respect `mpl.is_interactive()` # to determine if they should trigger a draw. install_repl_displayhook() ################# REMAINING CONTENT GENERATED BY boilerplate.py ############## # Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Figure.figimage) def figimage( X, xo=0, yo=0, alpha=None, norm=None, cmap=None, vmin=None, vmax=None, origin=None, resize=False, **kwargs): return gcf().figimage( X, xo=xo, yo=yo, alpha=alpha, norm=norm, cmap=cmap, vmin=vmin, vmax=vmax, origin=origin, resize=resize, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Figure.text) def figtext(x, y, s, fontdict=None, **kwargs): return gcf().text(x, y, s, fontdict=fontdict, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Figure.gca) def gca(**kwargs): return gcf().gca(**kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Figure._gci) def gci(): return gcf()._gci()
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Figure.ginput) def ginput( n=1, timeout=30, show_clicks=True, mouse_add=MouseButton.LEFT, mouse_pop=MouseButton.RIGHT, mouse_stop=MouseButton.MIDDLE): return gcf().ginput( n=n, timeout=timeout, show_clicks=show_clicks, mouse_add=mouse_add, mouse_pop=mouse_pop, mouse_stop=mouse_stop)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Figure.subplots_adjust) def subplots_adjust( left=None, bottom=None, right=None, top=None, wspace=None, hspace=None): return gcf().subplots_adjust( left=left, bottom=bottom, right=right, top=top, wspace=wspace, hspace=hspace)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Figure.suptitle) def suptitle(t, **kwargs): return gcf().suptitle(t, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Figure.waitforbuttonpress) def waitforbuttonpress(timeout=-1): return gcf().waitforbuttonpress(timeout=timeout)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.acorr) def acorr(x, *, data=None, **kwargs): return gca().acorr( x, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.angle_spectrum) def angle_spectrum( x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, *, data=None, **kwargs): return gca().angle_spectrum( x, Fs=Fs, Fc=Fc, window=window, pad_to=pad_to, sides=sides, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.annotate) def annotate(text, xy, *args, **kwargs): return gca().annotate(text, xy, *args, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.arrow) def arrow(x, y, dx, dy, **kwargs): return gca().arrow(x, y, dx, dy, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.autoscale) def autoscale(enable=True, axis='both', tight=None): return gca().autoscale(enable=enable, axis=axis, tight=tight)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.axhline) def axhline(y=0, xmin=0, xmax=1, **kwargs): return gca().axhline(y=y, xmin=xmin, xmax=xmax, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.axhspan) def axhspan(ymin, ymax, xmin=0, xmax=1, **kwargs): return gca().axhspan(ymin, ymax, xmin=xmin, xmax=xmax, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.axis) def axis(*args, emit=True, **kwargs): return gca().axis(*args, emit=emit, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.axline) def axline(xy1, xy2=None, *, slope=None, **kwargs): return gca().axline(xy1, xy2=xy2, slope=slope, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.axvline) def axvline(x=0, ymin=0, ymax=1, **kwargs): return gca().axvline(x=x, ymin=ymin, ymax=ymax, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.axvspan) def axvspan(xmin, xmax, ymin=0, ymax=1, **kwargs): return gca().axvspan(xmin, xmax, ymin=ymin, ymax=ymax, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.bar) def bar( x, height, width=0.8, bottom=None, *, align='center', data=None, **kwargs): return gca().bar( x, height, width=width, bottom=bottom, align=align, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.barbs) def barbs(*args, data=None, **kw): return gca().barbs( *args, **({"data": data} if data is not None else {}), **kw)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.barh) def barh(y, width, height=0.8, left=None, *, align='center', **kwargs): return gca().barh( y, width, height=height, left=left, align=align, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.boxplot) def boxplot( x, notch=None, sym=None, vert=None, whis=None, positions=None, widths=None, patch_artist=None, bootstrap=None, usermedians=None, conf_intervals=None, meanline=None, showmeans=None, showcaps=None, showbox=None, showfliers=None, boxprops=None, labels=None, flierprops=None, medianprops=None, meanprops=None, capprops=None, whiskerprops=None, manage_ticks=True, autorange=False, zorder=None, *, data=None): return gca().boxplot( x, notch=notch, sym=sym, vert=vert, whis=whis, positions=positions, widths=widths, patch_artist=patch_artist, bootstrap=bootstrap, usermedians=usermedians, conf_intervals=conf_intervals, meanline=meanline, showmeans=showmeans, showcaps=showcaps, showbox=showbox, showfliers=showfliers, boxprops=boxprops, labels=labels, flierprops=flierprops, medianprops=medianprops, meanprops=meanprops, capprops=capprops, whiskerprops=whiskerprops, manage_ticks=manage_ticks, autorange=autorange, zorder=zorder, **({"data": data} if data is not None else {}))
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.broken_barh) def broken_barh(xranges, yrange, *, data=None, **kwargs): return gca().broken_barh( xranges, yrange, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.cla) def cla(): return gca().cla()
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.clabel) def clabel(CS, levels=None, **kwargs): return gca().clabel(CS, levels=levels, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.cohere) def cohere( x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none, window=mlab.window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, *, data=None, **kwargs): return gca().cohere( x, y, NFFT=NFFT, Fs=Fs, Fc=Fc, detrend=detrend, window=window, noverlap=noverlap, pad_to=pad_to, sides=sides, scale_by_freq=scale_by_freq, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.contour) def contour(*args, data=None, **kwargs): __ret = gca().contour( *args, **({"data": data} if data is not None else {}), **kwargs) if __ret._A is not None: sci(__ret) # noqa return __ret
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.contourf) def contourf(*args, data=None, **kwargs): __ret = gca().contourf( *args, **({"data": data} if data is not None else {}), **kwargs) if __ret._A is not None: sci(__ret) # noqa return __ret
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.csd) def csd( x, y, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, pad_to=None, sides=None, scale_by_freq=None, return_line=None, *, data=None, **kwargs): return gca().csd( x, y, NFFT=NFFT, Fs=Fs, Fc=Fc, detrend=detrend, window=window, noverlap=noverlap, pad_to=pad_to, sides=sides, scale_by_freq=scale_by_freq, return_line=return_line, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.errorbar) def errorbar( x, y, yerr=None, xerr=None, fmt='', ecolor=None, elinewidth=None, capsize=None, barsabove=False, lolims=False, uplims=False, xlolims=False, xuplims=False, errorevery=1, capthick=None, *, data=None, **kwargs): return gca().errorbar( x, y, yerr=yerr, xerr=xerr, fmt=fmt, ecolor=ecolor, elinewidth=elinewidth, capsize=capsize, barsabove=barsabove, lolims=lolims, uplims=uplims, xlolims=xlolims, xuplims=xuplims, errorevery=errorevery, capthick=capthick, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.eventplot) def eventplot( positions, orientation='horizontal', lineoffsets=1, linelengths=1, linewidths=None, colors=None, linestyles='solid', *, data=None, **kwargs): return gca().eventplot( positions, orientation=orientation, lineoffsets=lineoffsets, linelengths=linelengths, linewidths=linewidths, colors=colors, linestyles=linestyles, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.fill) def fill(*args, data=None, **kwargs): return gca().fill( *args, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.fill_between) def fill_between( x, y1, y2=0, where=None, interpolate=False, step=None, *, data=None, **kwargs): return gca().fill_between( x, y1, y2=y2, where=where, interpolate=interpolate, step=step, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.fill_betweenx) def fill_betweenx( y, x1, x2=0, where=None, step=None, interpolate=False, *, data=None, **kwargs): return gca().fill_betweenx( y, x1, x2=x2, where=where, step=step, interpolate=interpolate, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.grid) def grid(b=None, which='major', axis='both', **kwargs): return gca().grid(b=b, which=which, axis=axis, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.hexbin) def hexbin( x, y, C=None, gridsize=100, bins=None, xscale='linear', yscale='linear', extent=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, edgecolors='face', reduce_C_function=np.mean, mincnt=None, marginals=False, *, data=None, **kwargs): __ret = gca().hexbin( x, y, C=C, gridsize=gridsize, bins=bins, xscale=xscale, yscale=yscale, extent=extent, cmap=cmap, norm=norm, vmin=vmin, vmax=vmax, alpha=alpha, linewidths=linewidths, edgecolors=edgecolors, reduce_C_function=reduce_C_function, mincnt=mincnt, marginals=marginals, **({"data": data} if data is not None else {}), **kwargs) sci(__ret) return __ret
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.hist) def hist( x, bins=None, range=None, density=False, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, *, data=None, **kwargs): return gca().hist( x, bins=bins, range=range, density=density, weights=weights, cumulative=cumulative, bottom=bottom, histtype=histtype, align=align, orientation=orientation, rwidth=rwidth, log=log, color=color, label=label, stacked=stacked, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.hist2d) def hist2d( x, y, bins=10, range=None, density=False, weights=None, cmin=None, cmax=None, *, data=None, **kwargs): __ret = gca().hist2d( x, y, bins=bins, range=range, density=density, weights=weights, cmin=cmin, cmax=cmax, **({"data": data} if data is not None else {}), **kwargs) sci(__ret[-1]) return __ret
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.hlines) def hlines( y, xmin, xmax, colors=None, linestyles='solid', label='', *, data=None, **kwargs): return gca().hlines( y, xmin, xmax, colors=colors, linestyles=linestyles, label=label, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.imshow) def imshow( X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=None, vmin=None, vmax=None, origin=None, extent=None, *, filternorm=True, filterrad=4.0, resample=None, url=None, data=None, **kwargs): __ret = gca().imshow( X, cmap=cmap, norm=norm, aspect=aspect, interpolation=interpolation, alpha=alpha, vmin=vmin, vmax=vmax, origin=origin, extent=extent, filternorm=filternorm, filterrad=filterrad, resample=resample, url=url, **({"data": data} if data is not None else {}), **kwargs) sci(__ret) return __ret
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.legend) def legend(*args, **kwargs): return gca().legend(*args, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.locator_params) def locator_params(axis='both', tight=None, **kwargs): return gca().locator_params(axis=axis, tight=tight, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.loglog) def loglog(*args, **kwargs): return gca().loglog(*args, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.magnitude_spectrum) def magnitude_spectrum( x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, scale=None, *, data=None, **kwargs): return gca().magnitude_spectrum( x, Fs=Fs, Fc=Fc, window=window, pad_to=pad_to, sides=sides, scale=scale, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.margins) def margins(*margins, x=None, y=None, tight=True): return gca().margins(*margins, x=x, y=y, tight=tight)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.minorticks_off) def minorticks_off(): return gca().minorticks_off()
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.minorticks_on) def minorticks_on(): return gca().minorticks_on()
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.pcolor) def pcolor( *args, shading=None, alpha=None, norm=None, cmap=None, vmin=None, vmax=None, data=None, **kwargs): __ret = gca().pcolor( *args, shading=shading, alpha=alpha, norm=norm, cmap=cmap, vmin=vmin, vmax=vmax, **({"data": data} if data is not None else {}), **kwargs) sci(__ret) return __ret
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.pcolormesh) def pcolormesh( *args, alpha=None, norm=None, cmap=None, vmin=None, vmax=None, shading=None, antialiased=False, data=None, **kwargs): __ret = gca().pcolormesh( *args, alpha=alpha, norm=norm, cmap=cmap, vmin=vmin, vmax=vmax, shading=shading, antialiased=antialiased, **({"data": data} if data is not None else {}), **kwargs) sci(__ret) return __ret
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.phase_spectrum) def phase_spectrum( x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, *, data=None, **kwargs): return gca().phase_spectrum( x, Fs=Fs, Fc=Fc, window=window, pad_to=pad_to, sides=sides, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.pie) def pie( x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=0, radius=1, counterclock=True, wedgeprops=None, textprops=None, center=(0, 0), frame=False, rotatelabels=False, *, normalize=None, data=None): return gca().pie( x, explode=explode, labels=labels, colors=colors, autopct=autopct, pctdistance=pctdistance, shadow=shadow, labeldistance=labeldistance, startangle=startangle, radius=radius, counterclock=counterclock, wedgeprops=wedgeprops, textprops=textprops, center=center, frame=frame, rotatelabels=rotatelabels, normalize=normalize, **({"data": data} if data is not None else {}))
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.plot) def plot(*args, scalex=True, scaley=True, data=None, **kwargs): return gca().plot( *args, scalex=scalex, scaley=scaley, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.plot_date) def plot_date( x, y, fmt='o', tz=None, xdate=True, ydate=False, *, data=None, **kwargs): return gca().plot_date( x, y, fmt=fmt, tz=tz, xdate=xdate, ydate=ydate, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.psd) def psd( x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, pad_to=None, sides=None, scale_by_freq=None, return_line=None, *, data=None, **kwargs): return gca().psd( x, NFFT=NFFT, Fs=Fs, Fc=Fc, detrend=detrend, window=window, noverlap=noverlap, pad_to=pad_to, sides=sides, scale_by_freq=scale_by_freq, return_line=return_line, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.quiver) def quiver(*args, data=None, **kw): __ret = gca().quiver( *args, **({"data": data} if data is not None else {}), **kw) sci(__ret) return __ret
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.quiverkey) def quiverkey(Q, X, Y, U, label, **kw): return gca().quiverkey(Q, X, Y, U, label, **kw)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.scatter) def scatter( x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=cbook.deprecation._deprecated_parameter, edgecolors=None, *, plotnonfinite=False, data=None, **kwargs): __ret = gca().scatter( x, y, s=s, c=c, marker=marker, cmap=cmap, norm=norm, vmin=vmin, vmax=vmax, alpha=alpha, linewidths=linewidths, verts=verts, edgecolors=edgecolors, plotnonfinite=plotnonfinite, **({"data": data} if data is not None else {}), **kwargs) sci(__ret) return __ret
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.semilogx) def semilogx(*args, **kwargs): return gca().semilogx(*args, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.semilogy) def semilogy(*args, **kwargs): return gca().semilogy(*args, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.specgram) def specgram( x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, cmap=None, xextent=None, pad_to=None, sides=None, scale_by_freq=None, mode=None, scale=None, vmin=None, vmax=None, *, data=None, **kwargs): __ret = gca().specgram( x, NFFT=NFFT, Fs=Fs, Fc=Fc, detrend=detrend, window=window, noverlap=noverlap, cmap=cmap, xextent=xextent, pad_to=pad_to, sides=sides, scale_by_freq=scale_by_freq, mode=mode, scale=scale, vmin=vmin, vmax=vmax, **({"data": data} if data is not None else {}), **kwargs) sci(__ret[-1]) return __ret
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.spy) def spy( Z, precision=0, marker=None, markersize=None, aspect='equal', origin='upper', **kwargs): __ret = gca().spy( Z, precision=precision, marker=marker, markersize=markersize, aspect=aspect, origin=origin, **kwargs) if isinstance(__ret, cm.ScalarMappable): sci(__ret) # noqa return __ret
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.stackplot) def stackplot( x, *args, labels=(), colors=None, baseline='zero', data=None, **kwargs): return gca().stackplot( x, *args, labels=labels, colors=colors, baseline=baseline, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.stem) def stem( *args, linefmt=None, markerfmt=None, basefmt=None, bottom=0, label=None, use_line_collection=True, data=None): return gca().stem( *args, linefmt=linefmt, markerfmt=markerfmt, basefmt=basefmt, bottom=bottom, label=label, use_line_collection=use_line_collection, **({"data": data} if data is not None else {}))
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.step) def step(x, y, *args, where='pre', data=None, **kwargs): return gca().step( x, y, *args, where=where, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.streamplot) def streamplot( x, y, u, v, density=1, linewidth=None, color=None, cmap=None, norm=None, arrowsize=1, arrowstyle='-|>', minlength=0.1, transform=None, zorder=None, start_points=None, maxlength=4.0, integration_direction='both', *, data=None): __ret = gca().streamplot( x, y, u, v, density=density, linewidth=linewidth, color=color, cmap=cmap, norm=norm, arrowsize=arrowsize, arrowstyle=arrowstyle, minlength=minlength, transform=transform, zorder=zorder, start_points=start_points, maxlength=maxlength, integration_direction=integration_direction, **({"data": data} if data is not None else {})) sci(__ret.lines) return __ret
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.table) def table( cellText=None, cellColours=None, cellLoc='right', colWidths=None, rowLabels=None, rowColours=None, rowLoc='left', colLabels=None, colColours=None, colLoc='center', loc='bottom', bbox=None, edges='closed', **kwargs): return gca().table( cellText=cellText, cellColours=cellColours, cellLoc=cellLoc, colWidths=colWidths, rowLabels=rowLabels, rowColours=rowColours, rowLoc=rowLoc, colLabels=colLabels, colColours=colColours, colLoc=colLoc, loc=loc, bbox=bbox, edges=edges, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.text) def text(x, y, s, fontdict=None, **kwargs): return gca().text(x, y, s, fontdict=fontdict, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.tick_params) def tick_params(axis='both', **kwargs): return gca().tick_params(axis=axis, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.ticklabel_format) def ticklabel_format( *, axis='both', style='', scilimits=None, useOffset=None, useLocale=None, useMathText=None): return gca().ticklabel_format( axis=axis, style=style, scilimits=scilimits, useOffset=useOffset, useLocale=useLocale, useMathText=useMathText)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.tricontour) def tricontour(*args, **kwargs): __ret = gca().tricontour(*args, **kwargs) if __ret._A is not None: sci(__ret) # noqa return __ret
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.tricontourf) def tricontourf(*args, **kwargs): __ret = gca().tricontourf(*args, **kwargs) if __ret._A is not None: sci(__ret) # noqa return __ret
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.tripcolor) def tripcolor( *args, alpha=1.0, norm=None, cmap=None, vmin=None, vmax=None, shading='flat', facecolors=None, **kwargs): __ret = gca().tripcolor( *args, alpha=alpha, norm=norm, cmap=cmap, vmin=vmin, vmax=vmax, shading=shading, facecolors=facecolors, **kwargs) sci(__ret) return __ret
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.triplot) def triplot(*args, **kwargs): return gca().triplot(*args, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.violinplot) def violinplot( dataset, positions=None, vert=True, widths=0.5, showmeans=False, showextrema=True, showmedians=False, quantiles=None, points=100, bw_method=None, *, data=None): return gca().violinplot( dataset, positions=positions, vert=vert, widths=widths, showmeans=showmeans, showextrema=showextrema, showmedians=showmedians, quantiles=quantiles, points=points, bw_method=bw_method, **({"data": data} if data is not None else {}))
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.vlines) def vlines( x, ymin, ymax, colors=None, linestyles='solid', label='', *, data=None, **kwargs): return gca().vlines( x, ymin, ymax, colors=colors, linestyles=linestyles, label=label, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.xcorr) def xcorr( x, y, normed=True, detrend=mlab.detrend_none, usevlines=True, maxlags=10, *, data=None, **kwargs): return gca().xcorr( x, y, normed=normed, detrend=detrend, usevlines=usevlines, maxlags=maxlags, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes._sci) def sci(im): return gca()._sci(im)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.set_title) def title(label, fontdict=None, loc=None, pad=None, *, y=None, **kwargs): return gca().set_title( label, fontdict=fontdict, loc=loc, pad=pad, y=y, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.set_xlabel) def xlabel(xlabel, fontdict=None, labelpad=None, *, loc=None, **kwargs): return gca().set_xlabel( xlabel, fontdict=fontdict, labelpad=labelpad, loc=loc, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.set_ylabel) def ylabel(ylabel, fontdict=None, labelpad=None, *, loc=None, **kwargs): return gca().set_ylabel( ylabel, fontdict=fontdict, labelpad=labelpad, loc=loc, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.set_xscale) def xscale(value, **kwargs): return gca().set_xscale(value, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]@_copy_docstring_and_deprecators(Axes.set_yscale) def yscale(value, **kwargs): return gca().set_yscale(value, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost.
[docs]def autumn(): set_cmap('autumn')
[docs]def bone(): set_cmap('bone')
[docs]def cool(): set_cmap('cool')
[docs]def copper(): set_cmap('copper')
[docs]def flag(): set_cmap('flag')
[docs]def gray(): set_cmap('gray')
[docs]def hot(): set_cmap('hot')
[docs]def hsv(): set_cmap('hsv')
[docs]def jet(): set_cmap('jet')
[docs]def pink(): set_cmap('pink')
[docs]def prism(): set_cmap('prism')
[docs]def spring(): set_cmap('spring')
[docs]def summer(): set_cmap('summer')
[docs]def winter(): set_cmap('winter')
[docs]def magma(): set_cmap('magma')
[docs]def inferno(): set_cmap('inferno')
[docs]def plasma(): set_cmap('plasma')
[docs]def viridis(): set_cmap('viridis')
[docs]def nipy_spectral(): set_cmap('nipy_spectral')
_setup_pyplot_info_docstrings()