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# Note: The first part of this file can be modified in place, but the latter # part is autogenerated by the boilerplate.py script.
`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. """
dedent, deprecated, silent_list, warn_deprecated, _string_to_bool)
# We may not need the following imports here:
FuncFormatter, FormatStrFormatter, ScalarFormatter,\ LogFormatter, LogFormatterExponent, LogFormatterMathtext,\ Locator, IndexLocator, FixedLocator, NullLocator,\ LinearLocator, LogLocator, AutoLocator, MultipleLocator,\ MaxNLocator
## Global ##
""" 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
# see if we have IPython hooks around, if use them
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:
# import failed or ipython is not running except (ImportError, _NotIPython): _INSTALL_FIG_OBSERVER = True
""" Uninstall the matplotlib display hook.
.. warning
Need IPython >= 2 for this to work. For IPython < 2 will raise a ``NotImplementedError``
.. warning
If you are using vanilla python and have installed another display hook this will reset ``sys.displayhook`` to what ever function was there when matplotlib installed it's displayhook, possibly discarding your changes. """ global _IP_REGISTERED global _INSTALL_FIG_OBSERVER if _IP_REGISTERED: from IPython import get_ipython ip = get_ipython() try: ip.events.unregister('post_execute', _IP_REGISTERED) except AttributeError: raise NotImplementedError("Can not unregister events " "in IPython < 2.0") _IP_REGISTERED = None
if _INSTALL_FIG_OBSERVER: _INSTALL_FIG_OBSERVER = False
if o is None: o = gcf() return o.findobj(match, include_self=include_self)
""" 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. """
for candidate in ["macosx", "qt5agg", "qt4agg", "gtk3agg", "gtk3cairo", "tkagg", "wxagg", "agg", "cairo"]: try: switch_backend(candidate) except ImportError: continue else: rcParamsOrig['backend'] = candidate return
newbackend[9:] if newbackend.startswith("module://") else "matplotlib.backends.backend_{}".format(newbackend.lower()))
"Backend", (matplotlib.backends._Backend,), vars(backend_mod)) newbackend, Backend.backend_version)
current_framework = \ matplotlib.backends._get_running_interactive_framework() if (current_framework and required_framework and current_framework != required_framework): raise ImportError( "Cannot load backend {!r} which requires the {!r} interactive " "framework, as {!r} is currently running".format( newbackend, required_framework, current_framework))
global _backend_mod, new_figure_manager, draw_if_interactive, _show
# Need to keep a global reference to the backend for compatibility reasons. # See https://github.com/matplotlib/matplotlib/issues/6092
""" Display a figure. When running in ipython with its pylab mode, display all figures and return to the ipython prompt.
In non-interactive mode, display all figures and block until the figures have been closed; in interactive mode it has no effect unless figures were created prior to a change from non-interactive to interactive mode (not recommended). In that case it displays the figures but does not block.
A single experimental keyword argument, *block*, may be set to True or False to override the blocking behavior described above. """ global _show return _show(*args, **kw)
"""Return the status of interactive mode.""" return matplotlib.is_interactive()
"""Turn the interactive mode off.""" matplotlib.interactive(False) uninstall_repl_displayhook()
"""Turn the interactive mode on.""" matplotlib.interactive(True) install_repl_displayhook()
""" Pause for *interval* seconds.
If there is an active figure, it will be updated and displayed before the pause, and the GUI event loop (if any) will run during the pause.
This can be used for crude animation. For more complex animation, see :mod:`matplotlib.animation`.
Notes ----- This function is experimental; its behavior may be changed or extended in a future release. """ manager = _pylab_helpers.Gcf.get_active() if manager is not None: canvas = manager.canvas if canvas.figure.stale: canvas.draw_idle() show(block=False) canvas.start_event_loop(interval) else: time.sleep(interval)
def rc(group, **kwargs): matplotlib.rc(group, **kwargs)
return matplotlib.rc_context(rc, fname)
def rcdefaults(): matplotlib.rcdefaults() if matplotlib.is_interactive(): draw_all()
## Current image ##
""" Get the current colorable artist. Specifically, returns the current :class:`~matplotlib.cm.ScalarMappable` instance (image or patch collection), or *None* if no images or patch collections have been defined. The commands :func:`~matplotlib.pyplot.imshow` and :func:`~matplotlib.pyplot.figimage` create :class:`~matplotlib.image.Image` instances, and the commands :func:`~matplotlib.pyplot.pcolor` and :func:`~matplotlib.pyplot.scatter` create :class:`~matplotlib.collections.Collection` instances. The current image is an attribute of the current axes, or the nearest earlier axes in the current figure that contains an image. """ return gcf()._gci()
## Any Artist ##
# (getp is simply imported) def setp(obj, *args, **kwargs): return _setp(obj, *args, **kwargs)
""" Turn on `xkcd <https://xkcd.com/>`_ sketch-style drawing mode. This will only have effect on things drawn after this function is called.
For best results, the "Humor Sans" font should be installed: it is not included with matplotlib.
Parameters ---------- scale : float, optional The amplitude of the wiggle perpendicular to the source line. length : float, optional The length of the wiggle along the line. randomness : float, optional The scale factor by which the length is shrunken or expanded.
Notes ----- This function works by a number of rcParams, so it will probably override others you have set before.
If you want the effects of this function to be temporary, it can be used as a context manager, for example::
with plt.xkcd(): # This figure will be in XKCD-style fig1 = plt.figure() # ...
# This figure will be in regular style fig2 = plt.figure() """ if rcParams['text.usetex']: raise RuntimeError( "xkcd mode is not compatible with text.usetex = True")
from matplotlib import patheffects return rc_context({ 'font.family': ['xkcd', 'Humor Sans', 'Comic Sans MS', 'StayPuft'], 'font.size': 14.0, 'path.sketch': (scale, length, randomness), 'path.effects': [patheffects.withStroke(linewidth=4, foreground="w")], 'axes.linewidth': 1.5, 'lines.linewidth': 2.0, 'figure.facecolor': 'white', 'grid.linewidth': 0.0, 'axes.grid': False, 'axes.unicode_minus': False, 'axes.edgecolor': 'black', 'xtick.major.size': 8, 'xtick.major.width': 3, 'ytick.major.size': 8, 'ytick.major.width': 3, })
## Figures ##
figsize=None, # defaults to rc figure.figsize dpi=None, # defaults to rc figure.dpi facecolor=None, # defaults to rc figure.facecolor edgecolor=None, # defaults to rc figure.edgecolor frameon=True, FigureClass=Figure, clear=False, **kwargs ): """ Create a new figure.
Parameters ----------
num : integer or string, optional, default: None If not provided, a new figure will be created, and the figure number will be incremented. The figure objects holds this number in a `number` attribute. If num is provided, and a figure with this id already exists, make it active, and returns a reference to it. If this figure does not exists, create it and returns it. If num is a string, the window title will be set to this figure's `num`.
figsize : tuple of integers, optional, default: None width, height in inches. If not provided, defaults to :rc:`figure.figsize` = ``[6.4, 4.8]``.
dpi : integer, optional, default: None resolution of the figure. If not provided, defaults to :rc:`figure.dpi` = ``100``.
facecolor : the background color. If not provided, defaults to :rc:`figure.facecolor` = ``'w'``.
edgecolor : the border color. If not provided, defaults to :rc:`figure.edgecolor` = ``'w'``.
frameon : bool, optional, default: True If False, suppress drawing the figure frame.
FigureClass : subclass of `~matplotlib.figure.Figure` Optionally use a custom `.Figure` instance.
clear : bool, optional, default: False If True and the figure already exists, then it is cleared.
Returns ------- figure : `~matplotlib.figure.Figure` The `.Figure` instance returned will also be passed to new_figure_manager in the backends, which allows to hook custom `.Figure` classes into the pyplot interface. Additional kwargs will be passed to the `.Figure` init function.
Notes ----- If you are creating many figures, make sure you explicitly call :func:`.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. """
elif isinstance(num, str): figLabel = num allLabels = get_figlabels() if figLabel not in allLabels: if figLabel == 'all': warnings.warn("close('all') closes all existing figures") num = next_num else: inum = allLabels.index(figLabel) num = allnums[inum] else: num = int(num) # crude validation of num argument
warnings.warn( "More than %d figures have been opened. Figures " "created through the pyplot interface " "(`matplotlib.pyplot.figure`) are retained until " "explicitly closed and may consume too much memory. " "(To control this warning, see the rcParam " "`figure.max_open_warning`)." % max_open_warning, RuntimeWarning)
dpi = 72
dpi=dpi, facecolor=facecolor, edgecolor=edgecolor, frameon=frameon, FigureClass=FigureClass, **kwargs)
figManager.set_window_title(figLabel) figManager.canvas.figure.set_label(figLabel)
# make this figure current on button press event _pylab_helpers.Gcf.set_active(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()
figManager.canvas.figure.clear()
""" This is an internal helper function for making sure that auto-redrawing works as intended in the plain python repl.
Parameters ---------- fig : Figure A figure object which is assumed to be associated with a canvas """ fig.canvas.draw_idle()
"""Get a reference to the current figure.""" figManager = _pylab_helpers.Gcf.get_active() if figManager is not None: return figManager.canvas.figure else: return figure()
"""Return whether the figure with the given id exists.""" return _pylab_helpers.Gcf.has_fignum(num) or num in get_figlabels()
"""Return a list of existing figure numbers."""
"""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]
""" Return the figure manager of the active figure.
If there is currently no active figure, a new one is created. """ figManager = _pylab_helpers.Gcf.get_active() if figManager is None: gcf() # creates an active figure as a side effect figManager = _pylab_helpers.Gcf.get_active() return figManager
def connect(s, func): return get_current_fig_manager().canvas.mpl_connect(s, func)
def disconnect(cid): return get_current_fig_manager().canvas.mpl_disconnect(cid)
""" 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
""" figManager = _pylab_helpers.Gcf.get_active() if figManager is None: return else: _pylab_helpers.Gcf.destroy(figManager.num) _pylab_helpers.Gcf.destroy(fig) # if we are dealing with a type UUID, we # can use its integer representation _pylab_helpers.Gcf.destroy(fig.int) allLabels = get_figlabels() if fig in allLabels: num = get_fignums()[allLabels.index(fig)] _pylab_helpers.Gcf.destroy(num) else: raise TypeError("close() argument must be a Figure, an int, a string, " "or None, not '%s'")
"""Clear the current figure.""" gcf().clf()
"""Redraw the current figure.
This is used to update a figure that has been altered, but not automatically re-drawn. If interactive mode is on (:func:`.ion()`), this should be only rarely needed, but there may be ways to modify the state of a figure without marking it as `stale`. Please report these cases as bugs.
A more object-oriented alternative, given any :class:`~matplotlib.figure.Figure` instance, :attr:`fig`, that was created using a :mod:`~matplotlib.pyplot` function, is::
fig.canvas.draw_idle() """ get_current_fig_manager().canvas.draw_idle()
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
def ginput(*args, **kwargs): """ Blocking call to interact with the figure.
This will wait for *n* clicks from the user and return a list of the coordinates of each click.
If *timeout* is negative, does not timeout. """ return gcf().ginput(*args, **kwargs)
def waitforbuttonpress(*args, **kwargs): """ Blocking call to interact with the figure.
This will wait for *n* key or mouse clicks from the user and return a list containing True's for keyboard clicks and False's for mouse clicks.
If *timeout* is negative, does not timeout. """ return gcf().waitforbuttonpress(*args, **kwargs)
## Putting things in figures ##
def figtext(x, y, s, *args, **kwargs): return gcf().text(x, y, s, *args, **kwargs)
def suptitle(t, **kwargs): return gcf().suptitle(t, **kwargs)
def figimage(*args, **kwargs): return gcf().figimage(*args, **kwargs)
""" Place a legend in the figure.
*labels* a sequence of strings
*handles* a sequence of :class:`~matplotlib.lines.Line2D` or :class:`~matplotlib.patches.Patch` instances
*loc* can be a string or an integer specifying the legend location
A :class:`matplotlib.legend.Legend` instance is returned.
Examples --------
To make a legend from existing artists on every axes::
figlegend()
To make a legend for a list of lines and labels::
figlegend( (line1, line2, line3), ('label1', 'label2', 'label3'), 'upper right' )
.. seealso::
:func:`~matplotlib.pyplot.legend`
""" return gcf().legend(*args, **kwargs)
## Axes ##
""" 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, 4-tuple, Axes } 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. - `~.axes.Axes`: This is equivalent to `.pyplot.sca`. It sets the current axes to *arg*. Note: This implicitly changes the current figure to the parent of *arg*.
.. note:: The use of an `.axes.Axes` as an argument is deprecated and will be removed in v3.0. Please use `.pyplot.sca` instead.
projection : {None, 'aitoff', 'hammer', 'lambert', 'mollweide', \ 'polar', 'rectilinear', str}, optional The projection type of the `~.axes.Axes`. *str* is the name of a costum projection, see `~matplotlib.projections`. The default None results in a 'rectilinear' projection.
polar : boolean, optional If True, equivalent to projection='polar'.
sharex, sharey : `~.axes.Axes`, optional Share the x or y `~matplotlib.axis` with sharex and/or sharey. The axis will have the same limits, ticks, and scale as the axis of the shared axes.
label : str A label for the returned axes.
Other Parameters ---------------- **kwargs This method also takes the keyword arguments for the returned axes class. The keyword arguments for the rectilinear axes class `~.axes.Axes` can be found in the following table but there might also be other keyword arguments if another projection is used, see the actual axes class. %(Axes)s
Returns ------- axes : `~.axes.Axes` (or a subclass of `~.axes.Axes`) The returned axes class depends on the projection used. It is `~.axes.Axes` if rectilinear projection are used and `.projections.polar.PolarAxes` if polar projection are used.
Notes ----- If the figure already has a axes with key (*args*, *kwargs*) then it will simply make that axes current and return it. This behavior is deprecated. Meanwhile, if you do not want this behavior (i.e., you want to force the creation of a new axes), you must use a unique set of args and kwargs. The axes *label* attribute has been exposed for this purpose: if you want two axes that are otherwise identical to be added to the figure, make sure you give them unique labels.
See Also -------- .Figure.add_axes .pyplot.subplot .Figure.add_subplot .Figure.subplots .pyplot.subplots
Examples -------- ::
#Creating a new full window axes plt.axes()
#Creating a new axes with specified dimensions and some kwargs plt.axes((left, bottom, width, height), facecolor='w') """
if arg is None: return subplot(111, **kwargs)
if isinstance(arg, Axes): warn_deprecated("2.2", message="Using pyplot.axes(ax) with ax an Axes " "argument is deprecated. Please use " "pyplot.sca(ax) instead.") ax = arg sca(ax) return ax else: rect = arg return gcf().add_axes(rect, **kwargs)
""" Remove the `Axes` *ax* (defaulting to the current axes) from its figure.
A KeyError is raised if the axes doesn't exist. """ if ax is None: ax = gca() ax.figure.delaxes(ax)
""" Set the current Axes instance to *ax*.
The current Figure is updated to the parent of *ax*. """ managers = _pylab_helpers.Gcf.get_all_fig_managers() for m in managers: if ax in m.canvas.figure.axes: _pylab_helpers.Gcf.set_active(m) m.canvas.figure.sca(ax) return raise ValueError("Axes instance argument was not found in a figure")
""" Get the current :class:`~matplotlib.axes.Axes` instance on the current figure matching the given keyword args, or create one.
Examples -------- To get the current polar axes on the current figure::
plt.gca(projection='polar')
If the current axes doesn't exist, or isn't a polar one, the appropriate axes will be created and then returned.
See Also -------- matplotlib.figure.Figure.gca : The figure's gca method. """ return gcf().gca(**kwargs)
## More ways of creating axes ##
def subplot(*args, **kwargs): """ Add a subplot to the current figure.
Wrapper of `.Figure.add_subplot` with a difference in behavior explained in the notes section.
Call signatures::
subplot(nrows, ncols, index, **kwargs) subplot(pos, **kwargs) subplot(ax)
Parameters ---------- *args Either a 3-digit integer or three separate integers describing the position of the subplot. If the three integers are *nrows*, *ncols*, and *index* in order, the subplot will take the *index* position on a grid with *nrows* rows and *ncols* columns. *index* starts at 1 in the upper left corner and increases to the right.
*pos* is a three digit integer, where the first digit is the number of rows, the second the number of columns, and the third the index of the subplot. i.e. fig.add_subplot(235) is the same as fig.add_subplot(2, 3, 5). Note that all integers must be less than 10 for this form to work.
projection : {None, 'aitoff', 'hammer', 'lambert', 'mollweide', \ 'polar', 'rectilinear', str}, optional The projection type of the subplot (`~.axes.Axes`). *str* is the name of a costum projection, see `~matplotlib.projections`. The default None results in a 'rectilinear' projection.
polar : boolean, optional If True, equivalent to projection='polar'.
sharex, sharey : `~.axes.Axes`, optional Share the x or y `~matplotlib.axis` with sharex and/or sharey. The axis will have the same limits, ticks, and scale as the axis of the shared axes.
label : str A label for the returned axes.
Other Parameters ---------------- **kwargs This method also takes the keyword arguments for the returned axes base class. The keyword arguments for the rectilinear base class `~.axes.Axes` can be found in the following table but there might also be other keyword arguments if another projection is used. %(Axes)s
Returns ------- axes : an `.axes.SubplotBase` subclass of `~.axes.Axes` (or a subclass \ of `~.axes.Axes`)
The axes of the subplot. The returned axes base class depends on the projection used. It is `~.axes.Axes` if rectilinear projection are used and `.projections.polar.PolarAxes` if polar projection are used. The returned axes is then a subplot subclass of the base class.
Notes ----- Creating a subplot will delete any pre-existing subplot that overlaps with it beyond sharing a boundary::
import matplotlib.pyplot as plt # plot a line, implicitly creating a subplot(111) plt.plot([1,2,3]) # now create a subplot which represents the top plot of a grid # with 2 rows and 1 column. Since this subplot will overlap the # first, the plot (and its axes) previously created, will be removed plt.subplot(211)
If you do not want this behavior, use the `.Figure.add_subplot` method or the `.pyplot.axes` function instead.
If the figure already has a subplot with key (*args*, *kwargs*) then it will simply make that subplot current and return it. This behavior is deprecated. Meanwhile, if you do not want this behavior (i.e., you want to force the creation of a new suplot), 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): warnings.warn("The subplot index argument to subplot() appears " "to be a boolean. Did you intend to use subplots()?")
fig = gcf() a = fig.add_subplot(*args, **kwargs) bbox = a.bbox byebye = [] for other in fig.axes: if other == a: continue if bbox.fully_overlaps(other.bbox): byebye.append(other) for ax in byebye: delaxes(ax)
return a
subplot_kw=None, gridspec_kw=None, **fig_kw): """ Create a figure and a set of subplots.
This utility wrapper makes it convenient to create common layouts of subplots, including the enclosing figure object, in a single call.
Parameters ---------- nrows, ncols : int, optional, default: 1 Number of rows/columns of the subplot grid.
sharex, sharey : bool or {'none', 'all', 'row', 'col'}, default: False Controls sharing of properties among x (`sharex`) or y (`sharey`) axes:
- True or 'all': x- or y-axis will be shared among all subplots. - False or 'none': each subplot x- or y-axis will be independent. - 'row': each subplot row will share an x- or y-axis. - 'col': each subplot column will share an x- or y-axis.
When subplots have a shared x-axis along a column, only the x tick labels of the bottom subplot are created. Similarly, when subplots have a shared y-axis along a row, only the y tick labels of the first column subplot are created. To later turn other subplots' ticklabels on, use `~matplotlib.axes.Axes.tick_params`.
squeeze : bool, optional, default: True - If True, extra dimensions are squeezed out from the returned array of `~matplotlib.axes.Axes`:
- if only one subplot is constructed (nrows=ncols=1), the resulting single Axes object is returned as a scalar. - for Nx1 or 1xM subplots, the returned object is a 1D numpy object array of Axes objects. - for NxM, subplots with N>1 and M>1 are returned as a 2D array.
- If False, no squeezing at all is done: the returned Axes object is always a 2D array containing Axes instances, even if it ends up being 1x1.
num : integer or string, optional, default: None A `.pyplot.figure` keyword that sets the figure number or label.
subplot_kw : dict, optional Dict with keywords passed to the `~matplotlib.figure.Figure.add_subplot` call used to create each subplot.
gridspec_kw : dict, optional Dict with keywords passed to the `~matplotlib.gridspec.GridSpec` constructor used to create the grid the subplots are placed on.
**fig_kw : All additional keyword arguments are passed to the `.pyplot.figure` call.
Returns ------- fig : `~.figure.Figure`
ax : `.axes.Axes` object or array of Axes objects. *ax* can be either a single `~matplotlib.axes.Axes` object or an array of Axes objects if more than one subplot was created. The dimensions of the resulting array can be controlled with the squeeze keyword, see above.
Examples -------- ::
#First create some toy data: x = np.linspace(0, 2*np.pi, 400) y = np.sin(x**2)
#Creates just a figure and only one subplot fig, ax = plt.subplots() ax.plot(x, y) ax.set_title('Simple plot')
#Creates two subplots and unpacks the output array immediately f, (ax1, ax2) = plt.subplots(1, 2, sharey=True) ax1.plot(x, y) ax1.set_title('Sharing Y axis') ax2.scatter(x, y)
#Creates four polar axes, and accesses them through the returned array fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True)) axes[0, 0].plot(x, y) axes[1, 1].scatter(x, y)
#Share a X axis with each column of subplots plt.subplots(2, 2, sharex='col')
#Share a Y axis with each row of subplots plt.subplots(2, 2, sharey='row')
#Share both X and Y axes with all subplots plt.subplots(2, 2, sharex='all', sharey='all')
#Note that this is the same as plt.subplots(2, 2, sharex=True, sharey=True)
#Creates figure number 10 with a single subplot #and clears it if it already exists. fig, ax=plt.subplots(num=10, clear=True)
See Also -------- .pyplot.figure .pyplot.subplot .pyplot.axes .Figure.subplots .Figure.add_subplot
""" fig = figure(**fig_kw) axs = fig.subplots(nrows=nrows, ncols=ncols, sharex=sharex, sharey=sharey, squeeze=squeeze, subplot_kw=subplot_kw, gridspec_kw=gridspec_kw) return fig, axs
""" Create an axis at specific location inside a regular grid.
Parameters ---------- shape : sequence of 2 ints Shape of grid in which to place axis. First entry is number of rows, second entry is number of columns.
loc : sequence of 2 ints Location to place axis within grid. First entry is row number, second entry is column number.
rowspan : int Number of rows for the axis to span to the right.
colspan : int Number of columns for the axis to span downwards.
fig : `Figure`, optional Figure to place axis in. Defaults to current figure.
**kwargs Additional keyword arguments are handed to `add_subplot`.
Notes ----- The following call ::
subplot2grid(shape, loc, rowspan=1, colspan=1)
is identical to ::
gridspec=GridSpec(shape[0], shape[1]) subplotspec=gridspec.new_subplotspec(loc, rowspan, colspan) subplot(subplotspec) """
if fig is None: fig = gcf()
s1, s2 = shape subplotspec = GridSpec(s1, s2).new_subplotspec(loc, rowspan=rowspan, colspan=colspan) a = fig.add_subplot(subplotspec, **kwargs) bbox = a.bbox byebye = [] for other in fig.axes: if other == a: continue if bbox.fully_overlaps(other.bbox): byebye.append(other) for ax in byebye: delaxes(ax)
return a
""" Make a second axes that shares the *x*-axis. The new axes will overlay *ax* (or the current axes if *ax* is *None*). The ticks for *ax2* will be placed on the right, and the *ax2* instance is returned.
.. seealso::
:doc:`/gallery/subplots_axes_and_figures/two_scales`
""" if ax is None: ax = gca() ax1 = ax.twinx() return ax1
""" Make a second axes that shares the *y*-axis. The new axis will overlay *ax* (or the current axes if *ax* is *None*). The ticks for *ax2* will be placed on the top, and the *ax2* instance is returned. """ if ax is None: ax = gca() ax1 = ax.twiny() return ax1
wspace=None, hspace=None): """ Tune the subplot layout.
The parameter meanings (and suggested defaults) are::
left = 0.125 # the left side of the subplots of the figure right = 0.9 # the right side of the subplots of the figure bottom = 0.1 # the bottom of the subplots of the figure top = 0.9 # the top of the subplots of the figure wspace = 0.2 # the amount of width reserved for space between subplots, # expressed as a fraction of the average axis width hspace = 0.2 # the amount of height reserved for space between subplots, # expressed as a fraction of the average axis height
The actual defaults are controlled by the rc file """ fig = gcf() fig.subplots_adjust(left, bottom, right, top, wspace, hspace)
""" Launch a subplot tool window for a figure.
A :class:`matplotlib.widgets.SubplotTool` instance is returned. """ tbar = rcParams['toolbar'] # turn off the navigation toolbar for the toolfig rcParams['toolbar'] = 'None' if targetfig is None: manager = get_current_fig_manager() targetfig = manager.canvas.figure else: # find the manager for this figure for manager in _pylab_helpers.Gcf._activeQue: if manager.canvas.figure == targetfig: break else: raise RuntimeError('Could not find manager for targetfig')
toolfig = figure(figsize=(6,3)) toolfig.subplots_adjust(top=0.9) ret = SubplotTool(targetfig, toolfig) rcParams['toolbar'] = tbar _pylab_helpers.Gcf.set_active(manager) # restore the current figure return ret
""" Automatically adjust subplot parameters to give specified padding.
Parameters ---------- pad : float Padding between the figure edge and the edges of subplots, as a fraction of the font size. h_pad, w_pad : float, optional Padding (height/width) between edges of adjacent subplots, as a fraction of the font size. Defaults to *pad*. rect : tuple (left, bottom, right, top), optional A rectangle (left, bottom, right, top) in the normalized figure coordinate that the whole subplots area (including labels) will fit into. Default is (0, 0, 1, 1). """ gcf().tight_layout(pad=pad, h_pad=h_pad, w_pad=w_pad, rect=rect)
""" 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() on = _string_to_bool(on) ax.set_frame_on(on)
## Axis ##
""" 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
""" 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
""" Get or set the current tick locations and labels of the x-axis.
Call signatures::
locs, labels = xticks() # Get locations and labels
xticks(ticks, [labels], **kwargs) # Set locations and labels
Parameters ---------- ticks : array_like A list of positions at which ticks should be placed. You can pass an empty list to disable xticks.
labels : array_like, optional A list of explicit labels to place at the given *locs*.
**kwargs :class:`.Text` properties can be used to control the appearance of the labels.
Returns ------- locs An array of label locations. labels A list of `.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 -------- Get the current locations and labels:
>>> locs, labels = xticks()
Set label locations:
>>> xticks(np.arange(0, 1, step=0.2))
Set text labels:
>>> xticks(np.arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue'))
Set text labels and properties:
>>> xticks(np.arange(12), calendar.month_name[1:13], rotation=20)
Disable xticks:
>>> xticks([]) """ ax = gca()
if ticks is None and labels is None: locs = ax.get_xticks() labels = ax.get_xticklabels() elif labels is None: locs = ax.set_xticks(ticks) labels = ax.get_xticklabels() else: locs = ax.set_xticks(ticks) labels = ax.set_xticklabels(labels, **kwargs) for l in labels: l.update(kwargs)
return locs, silent_list('Text xticklabel', labels)
""" Get or set the current tick locations and labels of the y-axis.
Call signatures::
locs, labels = yticks() # Get locations and labels
yticks(ticks, [labels], **kwargs) # Set locations and labels
Parameters ---------- ticks : array_like A list of positions at which ticks should be placed. You can pass an empty list to disable yticks.
labels : array_like, optional A list of explicit labels to place at the given *locs*.
**kwargs :class:`.Text` properties can be used to control the appearance of the labels.
Returns ------- locs An array of label locations. labels A list of `.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 -------- Get the current locations and labels:
>>> locs, labels = yticks()
Set label locations:
>>> yticks(np.arange(0, 1, step=0.2))
Set text labels:
>>> yticks(np.arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue'))
Set text labels and properties:
>>> yticks(np.arange(12), calendar.month_name[1:13], rotation=45)
Disable yticks:
>>> yticks([]) """ ax = gca()
if ticks is None and labels is None: locs = ax.get_yticks() labels = ax.get_yticklabels() elif labels is None: locs = ax.set_yticks(ticks) labels = ax.get_yticklabels() else: locs = ax.set_yticks(ticks) labels = ax.set_yticklabels(labels, **kwargs) for l in labels: l.update(kwargs)
return locs, silent_list('Text yticklabel', labels)
""" Get or set the radial gridlines on the current polar plot.
Call signatures::
lines, labels = rgrids() lines, labels = rgrids(radii, labels=None, angle=22.5, fmt=None, **kwargs)
When called with no arguments, `.rgrids` simply returns the tuple (*lines*, *labels*). When called with arguments, the labels will appear at the specified radial distances and angle.
Parameters ---------- radii : tuple with floats The radii for the radial gridlines
labels : tuple with strings or None The labels to use at each radial gridline. The `matplotlib.ticker.ScalarFormatter` will be used if None.
angle : float The angular position of the radius labels in degrees.
fmt : str or None Format string used in `matplotlib.ticker.FormatStrFormatter`. For example '%f'.
Returns ------- lines, labels : list of `.lines.Line2D`, list of `.text.Text` *lines* are the radial gridlines and *labels* are the tick labels.
Other Parameters ---------------- **kwargs *kwargs* are optional `~.Text` properties for the labels.
Examples -------- ::
# set the locations of the radial gridlines lines, labels = rgrids( (0.25, 0.5, 1.0) )
# set the locations and labels of the radial gridlines lines, labels = rgrids( (0.25, 0.5, 1.0), ('Tom', 'Dick', 'Harry' ))
See Also -------- .pyplot.thetagrids .projections.polar.PolarAxes.set_rgrids .Axis.get_gridlines .Axis.get_ticklabels
""" ax = gca() if not isinstance(ax, PolarAxes): raise RuntimeError('rgrids only defined for polar axes') if len(args)==0: lines = ax.yaxis.get_gridlines() labels = ax.yaxis.get_ticklabels() else: lines, labels = ax.set_rgrids(*args, **kwargs)
return ( silent_list('Line2D rgridline', lines), silent_list('Text rgridlabel', labels) )
""" Get or set the theta gridlines on the current polar plot.
Call signatures::
lines, labels = thetagrids() lines, labels = thetagrids(angles, labels=None, fmt=None, **kwargs)
When called with no arguments, `.thetagrids` simply returns the tuple (*lines*, *labels*). When called with arguments, the labels will appear at the specified angles.
Parameters ---------- angles : tuple with floats, degrees The angles of the theta gridlines.
labels : tuple with strings or None The labels to use at each radial gridline. The `.projections.polar.ThetaFormatter` will be used if None.
fmt : str or None Format string used in `matplotlib.ticker.FormatStrFormatter`. For example '%f'. Note that the angle in radians will be used.
Returns ------- lines, labels : list of `.lines.Line2D`, list of `.text.Text` *lines* are the theta gridlines and *labels* are the tick labels.
Other Parameters ---------------- **kwargs *kwargs* are optional `~.Text` properties for the labels.
Examples -------- ::
# set the locations of the angular gridlines lines, labels = thetagrids( range(45,360,90) )
# set the locations and labels of the angular gridlines lines, labels = thetagrids( range(45,360,90), ('NE', 'NW', 'SW','SE') )
See Also -------- .pyplot.rgrids .projections.polar.PolarAxes.set_thetagrids .Axis.get_gridlines .Axis.get_ticklabels """ ax = gca() if not isinstance(ax, PolarAxes): raise RuntimeError('thetagrids only defined for polar axes') if len(args)==0: lines = ax.xaxis.get_ticklines() labels = ax.xaxis.get_ticklabels() else: lines, labels = ax.set_thetagrids(*args, **kwargs)
return (silent_list('Line2D thetagridline', lines), silent_list('Text thetagridlabel', labels) )
## Plotting Info ##
pass
""" 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. 'get_plot_commands', 'get_current_fig_manager', 'ginput', 'plotting', 'waitforbuttonpress'} 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)
""" Matplotlib provides a number of colormaps, and others can be added using :func:`~matplotlib.cm.register_cmap`. This function documents the built-in colormaps, and will also return a list of all registered colormaps if called.
You can set the colormap for an image, pcolor, scatter, etc, using a keyword argument::
imshow(X, cmap=cm.hot)
or using the :func:`set_cmap` function::
imshow(X) pyplot.set_cmap('hot') pyplot.set_cmap('jet')
In interactive mode, :func:`set_cmap` will update the colormap post-hoc, allowing you to see which one works best for your data.
All built-in colormaps can be reversed by appending ``_r``: For instance, ``gray_r`` is the reverse of ``gray``.
There are several common color schemes used in visualization:
Sequential schemes for unipolar data that progresses from low to high Diverging schemes for bipolar data that emphasizes positive or negative deviations from a central value Cyclic schemes for plotting values that wrap around at the endpoints, such as phase angle, wind direction, or time of day Qualitative schemes for nominal data that has no inherent ordering, where color is used only to distinguish categories
Matplotlib ships with 4 perceptually uniform color maps which are the recommended color maps for sequential data:
========= =================================================== Colormap Description ========= =================================================== inferno perceptually uniform shades of black-red-yellow magma perceptually uniform shades of black-red-white plasma perceptually uniform shades of blue-red-yellow viridis perceptually uniform shades of blue-green-yellow ========= ===================================================
The following colormaps are based on the `ColorBrewer <http://colorbrewer2.org>`_ color specifications and designs developed by Cynthia Brewer:
ColorBrewer Diverging (luminance is highest at the midpoint, and decreases towards differently-colored endpoints):
======== =================================== Colormap Description ======== =================================== BrBG brown, white, blue-green PiYG pink, white, yellow-green PRGn purple, white, green PuOr orange, white, purple RdBu red, white, blue RdGy red, white, gray RdYlBu red, yellow, blue RdYlGn red, yellow, green Spectral red, orange, yellow, green, blue ======== ===================================
ColorBrewer Sequential (luminance decreases monotonically):
======== ==================================== Colormap Description ======== ==================================== Blues white to dark blue BuGn white, light blue, dark green BuPu white, light blue, dark purple GnBu white, light green, dark blue Greens white to dark green Greys white to black (not linear) Oranges white, orange, dark brown OrRd white, orange, dark red PuBu white, light purple, dark blue PuBuGn white, light purple, dark green PuRd white, light purple, dark red Purples white to dark purple RdPu white, pink, dark purple Reds white to dark red YlGn light yellow, dark green YlGnBu light yellow, light green, dark blue YlOrBr light yellow, orange, dark brown YlOrRd light yellow, orange, dark red ======== ====================================
ColorBrewer Qualitative:
(For plotting nominal data, :class:`ListedColormap` is used, not :class:`LinearSegmentedColormap`. Different sets of colors are recommended for different numbers of categories.)
* Accent * Dark2 * Paired * Pastel1 * Pastel2 * Set1 * Set2 * Set3
A set of colormaps derived from those of the same name provided with Matlab are also included:
========= ======================================================= Colormap Description ========= ======================================================= autumn sequential linearly-increasing shades of red-orange-yellow bone sequential increasing black-white color map with a tinge of blue, to emulate X-ray film cool linearly-decreasing shades of cyan-magenta copper sequential increasing shades of black-copper flag repetitive red-white-blue-black pattern (not cyclic at endpoints) gray sequential linearly-increasing black-to-white grayscale hot sequential black-red-yellow-white, to emulate blackbody radiation from an object at increasing temperatures jet a spectral map with dark endpoints, blue-cyan-yellow-red; based on a fluid-jet simulation by NCSA [#]_ pink sequential increasing pastel black-pink-white, meant for sepia tone colorization of photographs prism repetitive red-yellow-green-blue-purple-...-green pattern (not cyclic at endpoints) spring linearly-increasing shades of magenta-yellow summer sequential linearly-increasing shades of green-yellow winter linearly-increasing shades of blue-green ========= =======================================================
A set of palettes from the `Yorick scientific visualisation package <https://dhmunro.github.io/yorick-doc/>`_, an evolution of the GIST package, both by David H. Munro are included:
============ ======================================================= Colormap Description ============ ======================================================= gist_earth mapmaker's colors from dark blue deep ocean to green lowlands to brown highlands to white mountains gist_heat sequential increasing black-red-orange-white, to emulate blackbody radiation from an iron bar as it grows hotter gist_ncar pseudo-spectral black-blue-green-yellow-red-purple-white colormap from National Center for Atmospheric Research [#]_ gist_rainbow runs through the colors in spectral order from red to violet at full saturation (like *hsv* but not cyclic) gist_stern "Stern special" color table from Interactive Data Language software ============ =======================================================
A set of cyclic color maps:
================ ========================================================= Colormap Description ================ ========================================================= hsv red-yellow-green-cyan-blue-magenta-red, formed by changing the hue component in the HSV color space twilight perceptually uniform shades of white-blue-black-red-white twilight_shifted perceptually uniform shades of black-blue-white-red-black ================ =========================================================
Other miscellaneous schemes:
============= ======================================================= Colormap Description ============= ======================================================= afmhot sequential black-orange-yellow-white blackbody spectrum, commonly used in atomic force microscopy brg blue-red-green bwr diverging blue-white-red coolwarm diverging blue-gray-red, meant to avoid issues with 3D shading, color blindness, and ordering of colors [#]_ CMRmap "Default colormaps on color images often reproduce to confusing grayscale images. The proposed colormap maintains an aesthetically pleasing color image that automatically reproduces to a monotonic grayscale with discrete, quantifiable saturation levels." [#]_ cubehelix Unlike most other color schemes cubehelix was designed by D.A. Green to be monotonically increasing in terms of perceived brightness. Also, when printed on a black and white postscript printer, the scheme results in a greyscale with monotonically increasing brightness. This color scheme is named cubehelix because the r,g,b values produced can be visualised as a squashed helix around the diagonal in the r,g,b color cube. gnuplot gnuplot's traditional pm3d scheme (black-blue-red-yellow) gnuplot2 sequential color printable as gray (black-blue-violet-yellow-white) ocean green-blue-white rainbow spectral purple-blue-green-yellow-orange-red colormap with diverging luminance seismic diverging blue-white-red nipy_spectral black-purple-blue-green-yellow-red-white spectrum, originally from the Neuroimaging in Python project terrain mapmaker's colors, blue-green-yellow-brown-white, originally from IGOR Pro ============= =======================================================
The following colormaps are redundant and may be removed in future versions. It's recommended to use the names in the descriptions instead, which produce identical output:
========= ======================================================= Colormap Description ========= ======================================================= gist_gray identical to *gray* gist_yarg identical to *gray_r* binary identical to *gray_r* ========= =======================================================
.. rubric:: Footnotes
.. [#] Rainbow colormaps, ``jet`` in particular, are considered a poor choice for scientific visualization by many researchers: `Rainbow Color Map (Still) Considered Harmful <http://ieeexplore.ieee.org/document/4118486/?arnumber=4118486>`_
.. [#] Resembles "BkBlAqGrYeOrReViWh200" from NCAR Command Language. See `Color Table Gallery <https://www.ncl.ucar.edu/Document/Graphics/color_table_gallery.shtml>`_
.. [#] See `Diverging Color Maps for Scientific Visualization <http://www.kennethmoreland.com/color-maps/>`_ by Kenneth Moreland.
.. [#] See `A Color Map for Effective Black-and-White Rendering of Color-Scale Images <https://www.mathworks.com/matlabcentral/fileexchange/2662-cmrmap-m>`_ by Carey Rappaport """
""" Generates the plotting docstring.
These must be done after the entire module is imported, so it is called from the end of this module, which is generated by boilerplate.py. """
# Collect the first sentence of the docstring for all of the # plotting commands.
separator, '{:{}} {:{}}'.format('Function', max_name, 'Description', max_summary), separator, ] + [ '{:{}} {:{}}'.format(name, max_name, summary, max_summary) for name, summary in rows ] + [ separator, ]
## Plotting part 1: manually generated functions and wrappers ##
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
""" Set the color limits of the current image.
To apply clim to all axes images do::
clim(0, 0.5)
If either *vmin* or *vmax* is None, the image min/max respectively will be used for color scaling.
If you want to set the clim of multiple images, use, for example::
for im in gca().get_images(): im.set_clim(0, 0.05)
""" im = gci() if im is None: raise RuntimeError('You must first define an image, e.g., with imshow')
im.set_clim(vmin, vmax)
""" Set the default colormap. Applies to the current image if any. See help(colormaps) for more information.
*cmap* must be a :class:`~matplotlib.colors.Colormap` instance, or the name of a registered colormap.
See :func:`matplotlib.cm.register_cmap` and :func:`matplotlib.cm.get_cmap`. """ cmap = cm.get_cmap(cmap)
rc('image', cmap=cmap.name) im = gci()
if im is not None: im.set_cmap(cmap)
return matplotlib.image.imread(fname, format)
def imsave(fname, arr, **kwargs): return matplotlib.image.imsave(fname, arr, **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 *fignum* is an integer, draw into the figure with the given number (create it if it does not exist).
If 0 or *False*, use the current axes if it exists instead of creating a new figure.
.. note::
Because of how `.Axes.matshow` tries to set the figure aspect ratio to be the one of the array, strange things may happen if you reuse an existing figure.
Returns ------- image : `~matplotlib.image.AxesImage`
Other Parameters ---------------- **kwargs : `~matplotlib.axes.Axes.imshow` arguments
""" A = np.asanyarray(A) if fignum is False or fignum is 0: ax = gca() else: # Extract actual aspect ratio of array and make appropriately sized figure fig = figure(fignum, figsize=figaspect(A)) ax = fig.add_axes([0.15, 0.09, 0.775, 0.775])
im = ax.matshow(A, **kwargs) sci(im)
return im
""" Make a polar plot.
call signature::
polar(theta, r, **kwargs)
Multiple *theta*, *r* arguments are supported, with format strings, as in :func:`~matplotlib.pyplot.plot`.
""" # If an axis already exists, check if it has a polar projection if gcf().get_axes(): if not isinstance(gca(), PolarAxes): warnings.warn('Trying to create polar plot on an axis that does ' 'not have a polar projection.') ax = gca(polar=True) ret = ax.plot(*args, **kwargs) return ret
comments='#', skiprows=0, checkrows=5, delimiter=',', names=None, subplots=True, newfig=True, **kwargs): """ Plot the data in a file.
*cols* is a sequence of column identifiers to plot. An identifier is either an int or a string. If it is an int, it indicates the column number. If it is a string, it indicates the column header. matplotlib will make column headers lower case, replace spaces with underscores, and remove all illegal characters; so ``'Adj Close*'`` will have name ``'adj_close'``.
- If len(*cols*) == 1, only that column will be plotted on the *y* axis.
- If len(*cols*) > 1, the first element will be an identifier for data for the *x* axis and the remaining elements will be the column indexes for multiple subplots if *subplots* is *True* (the default), or for lines in a single subplot if *subplots* is *False*.
*plotfuncs*, if not *None*, is a dictionary mapping identifier to an :class:`~matplotlib.axes.Axes` plotting function as a string. Default is 'plot', other choices are 'semilogy', 'fill', 'bar', etc. You must use the same type of identifier in the *cols* vector as you use in the *plotfuncs* dictionary, e.g., integer column numbers in both or column names in both. If *subplots* is *False*, then including any function such as 'semilogy' that changes the axis scaling will set the scaling for all columns.
*comments*, *skiprows*, *checkrows*, *delimiter*, and *names* are all passed on to :func:`matplotlib.mlab.csv2rec` to load the data into a record array.
If *newfig* is *True*, the plot always will be made in a new figure; if *False*, it will be made in the current figure if one exists, else in a new figure.
kwargs are passed on to plotting functions.
Example usage::
# plot the 2nd and 4th column against the 1st in two subplots plotfile(fname, (0,1,3))
# plot using column names; specify an alternate plot type for volume plotfile(fname, ('date', 'volume', 'adj_close'), plotfuncs={'volume': 'semilogy'})
Note: plotfile is intended as a convenience for quickly plotting data from flat files; it is not intended as an alternative interface to general plotting with pyplot or matplotlib. """
if newfig: fig = figure() else: fig = gcf()
if len(cols)<1: raise ValueError('must have at least one column of data')
if plotfuncs is None: plotfuncs = dict() from matplotlib.cbook import MatplotlibDeprecationWarning with warnings.catch_warnings(): warnings.simplefilter('ignore', MatplotlibDeprecationWarning) r = mlab.csv2rec(fname, comments=comments, skiprows=skiprows, checkrows=checkrows, delimiter=delimiter, names=names)
def getname_val(identifier): 'return the name and column data for identifier' if isinstance(identifier, str): return identifier, r[identifier] elif isinstance(identifier, Number): name = r.dtype.names[int(identifier)] return name, r[name] else: raise TypeError('identifier must be a string or integer')
xname, x = getname_val(cols[0]) ynamelist = []
if len(cols)==1: ax1 = fig.add_subplot(1,1,1) funcname = plotfuncs.get(cols[0], 'plot') func = getattr(ax1, funcname) func(x, **kwargs) ax1.set_ylabel(xname) else: N = len(cols) for i in range(1,N): if subplots: if i==1: ax = ax1 = fig.add_subplot(N-1,1,i) else: ax = fig.add_subplot(N-1,1,i, sharex=ax1) elif i==1: ax = fig.add_subplot(1,1,1)
yname, y = getname_val(cols[i]) ynamelist.append(yname)
funcname = plotfuncs.get(cols[i], 'plot') func = getattr(ax, funcname)
func(x, y, **kwargs) if subplots: ax.set_ylabel(yname) if ax.is_last_row(): ax.set_xlabel(xname) else: ax.set_xlabel('')
if not subplots: ax.legend(ynamelist)
if xname=='date': fig.autofmt_xdate()
"""Autogenerated wrappers will get their docstring from a base function with an addendum."""
# 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. and dict.__getitem__(rcParams, "backend") in _interactive_bk and _get_running_interactive_framework()): dict.__setitem__(rcParams, "backend", rcsetup._auto_backend_sentinel) # Set up the 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.
################# REMAINING CONTENT GENERATED BY boilerplate.py ##############
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. 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. 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. def annotate(s, xy, *args, **kwargs): return gca().annotate(s, xy, *args, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. 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. return gca().autoscale(enable=enable, axis=axis, tight=tight)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. return gca().axhline(y=y, xmin=xmin, xmax=xmax, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. return gca().axhspan(ymin, ymax, xmin=xmin, xmax=xmax, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. def axis(*v, **kwargs): return gca().axis(*v, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. return gca().axvline(x=x, ymin=ymin, ymax=ymax, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. return gca().axvspan(xmin, xmax, ymin=ymin, ymax=ymax, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. 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. 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. return gca().barh( y, width, height=height, left=left, align=align, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. x, notch=None, sym=None, vert=None, whis=None, positions=None, widths=None, patch_artist=None, bootstrap=None, usermedians=None, conf_intervals=None, meanline=None, showmeans=None, showcaps=None, showbox=None, showfliers=None, boxprops=None, labels=None, flierprops=None, medianprops=None, meanprops=None, capprops=None, whiskerprops=None, manage_xticks=True, autorange=False, zorder=None, *, 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_xticks=manage_xticks, autorange=autorange, zorder=zorder, **({"data": data} if data is not None else {}))
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. 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. def cla(): return gca().cla()
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. def clabel(CS, *args, **kwargs): return gca().clabel(CS, *args, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. 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. __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. __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. 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. 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. 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. 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. 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. 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. return gca().grid(b=b, which=which, axis=axis, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. 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. x, bins=None, range=None, density=None, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, normed=None, *, 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, normed=normed, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. x, y, bins=10, range=None, normed=False, weights=None, cmin=None, cmax=None, *, data=None, **kwargs): __ret = gca().hist2d( x, y, bins=bins, range=range, normed=normed, 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. y, xmin, xmax, colors='k', linestyles='solid', label='', *, data=None, **kwargs): return gca().hlines( y, xmin, xmax, colors=colors, linestyles=linestyles, label=label, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=None, vmin=None, vmax=None, origin=None, extent=None, shape=None, filternorm=1, filterrad=4.0, imlim=None, resample=None, url=None, *, data=None, **kwargs): __ret = gca().imshow( X, cmap=cmap, norm=norm, aspect=aspect, interpolation=interpolation, alpha=alpha, vmin=vmin, vmax=vmax, origin=origin, extent=extent, shape=shape, filternorm=filternorm, filterrad=filterrad, imlim=imlim, resample=resample, url=url, **({"data": data} if data is not None else {}), **kwargs) sci(__ret) return __ret
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. def legend(*args, **kwargs): return gca().legend(*args, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. return gca().locator_params(axis=axis, tight=tight, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. def loglog(*args, **kwargs): return gca().loglog(*args, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. 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. return gca().margins(*margins, x=x, y=y, tight=tight)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. def minorticks_off(): return gca().minorticks_off()
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. def minorticks_on(): return gca().minorticks_on()
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. *args, alpha=None, norm=None, cmap=None, vmin=None, vmax=None, data=None, **kwargs): __ret = gca().pcolor( *args, alpha=alpha, norm=norm, cmap=cmap, vmin=vmin, vmax=vmax, **({"data": data} if data is not None else {}), **kwargs) sci(__ret) return __ret
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. *args, alpha=None, norm=None, cmap=None, vmin=None, vmax=None, shading='flat', antialiased=False, data=None, **kwargs): __ret = gca().pcolormesh( *args, alpha=alpha, norm=norm, cmap=cmap, vmin=vmin, vmax=vmax, shading=shading, antialiased=antialiased, **({"data": data} if data is not None else {}), **kwargs) sci(__ret) return __ret
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. 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. x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=None, radius=None, counterclock=True, wedgeprops=None, textprops=None, center=(0, 0), frame=False, rotatelabels=False, *, data=None): return gca().pie( x, explode=explode, labels=labels, colors=colors, autopct=autopct, pctdistance=pctdistance, shadow=shadow, labeldistance=labeldistance, startangle=startangle, radius=radius, counterclock=counterclock, wedgeprops=wedgeprops, textprops=textprops, center=center, frame=frame, rotatelabels=rotatelabels, **({"data": data} if data is not None else {}))
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. 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. 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. 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. __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. 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. x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, edgecolors=None, *, 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, **({"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. def semilogx(*args, **kwargs): return gca().semilogx(*args, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. def semilogy(*args, **kwargs): return gca().semilogy(*args, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. 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. 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. return gca().stackplot( x, *args, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. *args, linefmt=None, markerfmt=None, basefmt=None, bottom=0, label=None, data=None): return gca().stem( *args, linefmt=linefmt, markerfmt=markerfmt, basefmt=basefmt, bottom=bottom, label=label, **({"data": data} if data is not None else {}))
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. 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. 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. def table(**kwargs): return gca().table(**kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. return gca().text(x, y, s, fontdict=fontdict, withdash=withdash, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. return gca().tick_params(axis=axis, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. *, 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. 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. 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. def tripcolor(*args, **kwargs): __ret = gca().tripcolor(*args, **kwargs) sci(__ret) return __ret
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. def triplot(*args, **kwargs): return gca().triplot(*args, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. dataset, positions=None, vert=True, widths=0.5, showmeans=False, showextrema=True, showmedians=False, points=100, bw_method=None, *, data=None): return gca().violinplot( dataset, positions=positions, vert=vert, widths=widths, showmeans=showmeans, showextrema=showextrema, showmedians=showmedians, 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. x, ymin, ymax, colors='k', linestyles='solid', label='', *, data=None, **kwargs): return gca().vlines( x, ymin, ymax, colors=colors, linestyles=linestyles, label=label, **({"data": data} if data is not None else {}), **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. 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. def sci(im): return gca()._sci(im)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. return gca().set_title( label, fontdict=fontdict, loc=loc, pad=pad, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. return gca().set_xlabel( xlabel, fontdict=fontdict, labelpad=labelpad, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. return gca().set_ylabel( ylabel, fontdict=fontdict, labelpad=labelpad, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. def xscale(value, **kwargs): return gca().set_xscale(value, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. def yscale(value, **kwargs): return gca().set_yscale(value, **kwargs)
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. """ Set the colormap to "autumn".
This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ set_cmap("autumn")
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. """ Set the colormap to "bone".
This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ set_cmap("bone")
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. """ Set the colormap to "cool".
This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ set_cmap("cool")
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. """ Set the colormap to "copper".
This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ set_cmap("copper")
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. """ Set the colormap to "flag".
This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ set_cmap("flag")
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. """ Set the colormap to "gray".
This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ set_cmap("gray")
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. """ Set the colormap to "hot".
This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ set_cmap("hot")
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. """ Set the colormap to "hsv".
This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ set_cmap("hsv")
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. """ Set the colormap to "jet".
This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ set_cmap("jet")
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. """ Set the colormap to "pink".
This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ set_cmap("pink")
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. """ Set the colormap to "prism".
This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ set_cmap("prism")
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. """ Set the colormap to "spring".
This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ set_cmap("spring")
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. """ Set the colormap to "summer".
This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ set_cmap("summer")
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. """ Set the colormap to "winter".
This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ set_cmap("winter")
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. """ Set the colormap to "magma".
This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ set_cmap("magma")
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. """ Set the colormap to "inferno".
This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ set_cmap("inferno")
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. """ Set the colormap to "plasma".
This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ set_cmap("plasma")
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. """ Set the colormap to "viridis".
This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ set_cmap("viridis")
# Autogenerated by boilerplate.py. Do not edit as changes will be lost. """ Set the colormap to "nipy_spectral".
This changes the default colormap as well as the colormap of the current image if there is one. See ``help(colormaps)`` for more information. """ set_cmap("nipy_spectral") |