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import collections.abc 

import functools 

import itertools 

import logging 

import math 

from numbers import Number 

import warnings 

 

import numpy as np 

from numpy import ma 

 

import matplotlib 

from matplotlib import _preprocess_data 

 

import matplotlib.cbook as cbook 

import matplotlib.collections as mcoll 

import matplotlib.colors as mcolors 

import matplotlib.contour as mcontour 

import matplotlib.category as _ # <-registers a category unit converter 

import matplotlib.dates as _ # <-registers a date unit converter 

import matplotlib.docstring as docstring 

import matplotlib.image as mimage 

import matplotlib.legend as mlegend 

import matplotlib.lines as mlines 

import matplotlib.markers as mmarkers 

import matplotlib.mlab as mlab 

import matplotlib.path as mpath 

import matplotlib.patches as mpatches 

import matplotlib.quiver as mquiver 

import matplotlib.stackplot as mstack 

import matplotlib.streamplot as mstream 

import matplotlib.table as mtable 

import matplotlib.text as mtext 

import matplotlib.ticker as mticker 

import matplotlib.transforms as mtransforms 

import matplotlib.tri as mtri 

from matplotlib.cbook import ( 

MatplotlibDeprecationWarning, warn_deprecated, STEP_LOOKUP_MAP, iterable, 

safe_first_element) 

from matplotlib.container import BarContainer, ErrorbarContainer, StemContainer 

from matplotlib.axes._base import _AxesBase, _process_plot_format 

 

_log = logging.getLogger(__name__) 

 

rcParams = matplotlib.rcParams 

 

 

def _has_item(data, name): 

"""Return whether *data* can be item-accessed with *name*. 

 

This supports data with a dict-like interface (`in` checks item 

availability) and with numpy.arrays. 

""" 

try: 

return data.dtype.names is not None and name in data.dtype.names 

except AttributeError: # not a numpy array 

return name in data 

 

 

def _plot_args_replacer(args, data): 

if len(args) == 1: 

return ["y"] 

elif len(args) == 2: 

# this can be two cases: x,y or y,c 

if not _has_item(data, args[1]): 

return ["y", "c"] 

# it's data, but could be a color code like 'ro' or 'b--' 

# -> warn the user in that case... 

try: 

_process_plot_format(args[1]) 

except ValueError: 

pass 

else: 

cbook._warn_external( 

"Second argument {!r} is ambiguous: could be a color spec but " 

"is in data; using as data. Either rename the entry in data " 

"or use three arguments to plot.".format(args[1]), 

RuntimeWarning) 

return ["x", "y"] 

elif len(args) == 3: 

return ["x", "y", "c"] 

else: 

raise ValueError("Using arbitrary long args with data is not " 

"supported due to ambiguity of arguments.\nUse " 

"multiple plotting calls instead.") 

 

 

def _make_inset_locator(bounds, trans, parent): 

""" 

Helper function to locate inset axes, used in 

`.Axes.inset_axes`. 

 

A locator gets used in `Axes.set_aspect` to override the default 

locations... It is a function that takes an axes object and 

a renderer and tells `set_aspect` where it is to be placed. 

 

Here *rect* is a rectangle [l, b, w, h] that specifies the 

location for the axes in the transform given by *trans* on the 

*parent*. 

""" 

_bounds = mtransforms.Bbox.from_bounds(*bounds) 

_trans = trans 

_parent = parent 

 

def inset_locator(ax, renderer): 

bbox = _bounds 

bb = mtransforms.TransformedBbox(bbox, _trans) 

tr = _parent.figure.transFigure.inverted() 

bb = mtransforms.TransformedBbox(bb, tr) 

return bb 

 

return inset_locator 

 

 

# The axes module contains all the wrappers to plotting functions. 

# All the other methods should go in the _AxesBase class. 

 

 

class Axes(_AxesBase): 

""" 

The :class:`Axes` contains most of the figure elements: 

:class:`~matplotlib.axis.Axis`, :class:`~matplotlib.axis.Tick`, 

:class:`~matplotlib.lines.Line2D`, :class:`~matplotlib.text.Text`, 

:class:`~matplotlib.patches.Polygon`, etc., and sets the 

coordinate system. 

 

The :class:`Axes` instance supports callbacks through a callbacks 

attribute which is a :class:`~matplotlib.cbook.CallbackRegistry` 

instance. The events you can connect to are 'xlim_changed' and 

'ylim_changed' and the callback will be called with func(*ax*) 

where *ax* is the :class:`Axes` instance. 

 

Attributes 

---------- 

dataLim : `.BBox` 

The bounding box enclosing all data displayed in the Axes. 

viewLim : `.BBox` 

The view limits in data coordinates. 

 

""" 

### Labelling, legend and texts 

 

aname = 'Axes' 

 

def get_title(self, loc="center"): 

""" 

Get an axes title. 

 

Get one of the three available axes titles. The available titles 

are positioned above the axes in the center, flush with the left 

edge, and flush with the right edge. 

 

Parameters 

---------- 

loc : {'center', 'left', 'right'}, str, optional 

Which title to get, defaults to 'center'. 

 

Returns 

------- 

title : str 

The title text string. 

 

""" 

try: 

title = {'left': self._left_title, 

'center': self.title, 

'right': self._right_title}[loc.lower()] 

except KeyError: 

raise ValueError("'%s' is not a valid location" % loc) 

return title.get_text() 

 

def set_title(self, label, fontdict=None, loc="center", pad=None, 

**kwargs): 

""" 

Set a title for the axes. 

 

Set one of the three available axes titles. The available titles 

are positioned above the axes in the center, flush with the left 

edge, and flush with the right edge. 

 

Parameters 

---------- 

label : str 

Text to use for the title 

 

fontdict : dict 

A dictionary controlling the appearance of the title text, 

the default `fontdict` is:: 

 

{'fontsize': rcParams['axes.titlesize'], 

'fontweight' : rcParams['axes.titleweight'], 

'verticalalignment': 'baseline', 

'horizontalalignment': loc} 

 

loc : {'center', 'left', 'right'}, str, optional 

Which title to set, defaults to 'center' 

 

pad : float 

The offset of the title from the top of the axes, in points. 

Default is ``None`` to use rcParams['axes.titlepad']. 

 

Returns 

------- 

text : :class:`~matplotlib.text.Text` 

The matplotlib text instance representing the title 

 

Other Parameters 

---------------- 

**kwargs : `~matplotlib.text.Text` properties 

Other keyword arguments are text properties, see 

:class:`~matplotlib.text.Text` for a list of valid text 

properties. 

""" 

try: 

title = {'left': self._left_title, 

'center': self.title, 

'right': self._right_title}[loc.lower()] 

except KeyError: 

raise ValueError("'%s' is not a valid location" % loc) 

default = { 

'fontsize': rcParams['axes.titlesize'], 

'fontweight': rcParams['axes.titleweight'], 

'verticalalignment': 'baseline', 

'horizontalalignment': loc.lower()} 

if pad is None: 

pad = rcParams['axes.titlepad'] 

self._set_title_offset_trans(float(pad)) 

title.set_text(label) 

title.update(default) 

if fontdict is not None: 

title.update(fontdict) 

title.update(kwargs) 

return title 

 

def get_xlabel(self): 

""" 

Get the xlabel text string. 

""" 

label = self.xaxis.get_label() 

return label.get_text() 

 

def set_xlabel(self, xlabel, fontdict=None, labelpad=None, **kwargs): 

""" 

Set the label for the x-axis. 

 

Parameters 

---------- 

xlabel : str 

The label text. 

 

labelpad : scalar, optional, default: None 

Spacing in points between the label and the x-axis. 

 

Other Parameters 

---------------- 

**kwargs : `.Text` properties 

`.Text` properties control the appearance of the label. 

 

See also 

-------- 

text : for information on how override and the optional args work 

""" 

if labelpad is not None: 

self.xaxis.labelpad = labelpad 

return self.xaxis.set_label_text(xlabel, fontdict, **kwargs) 

 

def get_ylabel(self): 

""" 

Get the ylabel text string. 

""" 

label = self.yaxis.get_label() 

return label.get_text() 

 

def set_ylabel(self, ylabel, fontdict=None, labelpad=None, **kwargs): 

""" 

Set the label for the y-axis. 

 

Parameters 

---------- 

ylabel : str 

The label text. 

 

labelpad : scalar, optional, default: None 

Spacing in points between the label and the y-axis. 

 

Other Parameters 

---------------- 

**kwargs : `.Text` properties 

`.Text` properties control the appearance of the label. 

 

See also 

-------- 

text : for information on how override and the optional args work 

 

""" 

if labelpad is not None: 

self.yaxis.labelpad = labelpad 

return self.yaxis.set_label_text(ylabel, fontdict, **kwargs) 

 

def get_legend_handles_labels(self, legend_handler_map=None): 

""" 

Return handles and labels for legend 

 

``ax.legend()`` is equivalent to :: 

 

h, l = ax.get_legend_handles_labels() 

ax.legend(h, l) 

 

""" 

 

# pass through to legend. 

handles, labels = mlegend._get_legend_handles_labels([self], 

legend_handler_map) 

return handles, labels 

 

@docstring.dedent_interpd 

def legend(self, *args, **kwargs): 

""" 

Place a legend on the axes. 

 

Call signatures:: 

 

legend() 

legend(labels) 

legend(handles, labels) 

 

The call signatures correspond to three different ways how to use 

this method. 

 

**1. Automatic detection of elements to be shown in the legend** 

 

The elements to be added to the legend are automatically determined, 

when you do not pass in any extra arguments. 

 

In this case, the labels are taken from the artist. You can specify 

them either at artist creation or by calling the 

:meth:`~.Artist.set_label` method on the artist:: 

 

line, = ax.plot([1, 2, 3], label='Inline label') 

ax.legend() 

 

or:: 

 

line.set_label('Label via method') 

line, = ax.plot([1, 2, 3]) 

ax.legend() 

 

Specific lines can be excluded from the automatic legend element 

selection by defining a label starting with an underscore. 

This is default for all artists, so calling `Axes.legend` without 

any arguments and without setting the labels manually will result in 

no legend being drawn. 

 

 

**2. Labeling existing plot elements** 

 

To make a legend for lines which already exist on the axes 

(via plot for instance), simply call this function with an iterable 

of strings, one for each legend item. For example:: 

 

ax.plot([1, 2, 3]) 

ax.legend(['A simple line']) 

 

Note: This way of using is discouraged, because the relation between 

plot elements and labels is only implicit by their order and can 

easily be mixed up. 

 

 

**3. Explicitly defining the elements in the legend** 

 

For full control of which artists have a legend entry, it is possible 

to pass an iterable of legend artists followed by an iterable of 

legend labels respectively:: 

 

legend((line1, line2, line3), ('label1', 'label2', 'label3')) 

 

Parameters 

---------- 

 

handles : sequence of `.Artist`, optional 

A list of Artists (lines, patches) to be added to the legend. 

Use this together with *labels*, if you need full control on what 

is shown in the legend and the automatic mechanism described above 

is not sufficient. 

 

The length of handles and labels should be the same in this 

case. If they are not, they are truncated to the smaller length. 

 

labels : sequence of strings, optional 

A list of labels to show next to the artists. 

Use this together with *handles*, if you need full control on what 

is shown in the legend and the automatic mechanism described above 

is not sufficient. 

 

Other Parameters 

---------------- 

 

%(_legend_kw_doc)s 

 

Returns 

------- 

 

:class:`matplotlib.legend.Legend` instance 

 

Notes 

----- 

 

Not all kinds of artist are supported by the legend command. See 

:doc:`/tutorials/intermediate/legend_guide` for details. 

 

Examples 

-------- 

 

.. plot:: gallery/text_labels_and_annotations/legend.py 

 

""" 

handles, labels, extra_args, kwargs = mlegend._parse_legend_args( 

[self], 

*args, 

**kwargs) 

if len(extra_args): 

raise TypeError('legend only accepts two non-keyword arguments') 

self.legend_ = mlegend.Legend(self, handles, labels, **kwargs) 

self.legend_._remove_method = self._remove_legend 

return self.legend_ 

 

def _remove_legend(self, legend): 

self.legend_ = None 

 

def inset_axes(self, bounds, *, transform=None, zorder=5, 

**kwargs): 

""" 

Add a child inset axes to this existing axes. 

 

Warnings 

-------- 

 

This method is experimental as of 3.0, and the API may change. 

 

Parameters 

---------- 

 

bounds : [x0, y0, width, height] 

Lower-left corner of inset axes, and its width and height. 

 

transform : `.Transform` 

Defaults to `ax.transAxes`, i.e. the units of *rect* are in 

axes-relative coordinates. 

 

zorder : number 

Defaults to 5 (same as `.Axes.legend`). Adjust higher or lower 

to change whether it is above or below data plotted on the 

parent axes. 

 

**kwargs 

 

Other *kwargs* are passed on to the `axes.Axes` child axes. 

 

Returns 

------- 

 

Axes 

The created `.axes.Axes` instance. 

 

Examples 

-------- 

 

This example makes two inset axes, the first is in axes-relative 

coordinates, and the second in data-coordinates:: 

 

fig, ax = plt.suplots() 

ax.plot(range(10)) 

axin1 = ax.inset_axes([0.8, 0.1, 0.15, 0.15]) 

axin2 = ax.inset_axes( 

[5, 7, 2.3, 2.3], transform=ax.transData) 

 

""" 

if transform is None: 

transform = self.transAxes 

label = kwargs.pop('label', 'inset_axes') 

 

# This puts the rectangle into figure-relative coordinates. 

inset_locator = _make_inset_locator(bounds, transform, self) 

bb = inset_locator(None, None) 

 

inset_ax = Axes(self.figure, bb.bounds, zorder=zorder, 

label=label, **kwargs) 

 

# this locator lets the axes move if in data coordinates. 

# it gets called in `ax.apply_aspect() (of all places) 

inset_ax.set_axes_locator(inset_locator) 

 

self.add_child_axes(inset_ax) 

 

return inset_ax 

 

def indicate_inset(self, bounds, inset_ax=None, *, transform=None, 

facecolor='none', edgecolor='0.5', alpha=0.5, 

zorder=4.99, **kwargs): 

""" 

Add an inset indicator to the axes. This is a rectangle on the plot 

at the position indicated by *bounds* that optionally has lines that 

connect the rectangle to an inset axes 

(`.Axes.inset_axes`). 

 

Warnings 

-------- 

 

This method is experimental as of 3.0, and the API may change. 

 

 

Parameters 

---------- 

 

bounds : [x0, y0, width, height] 

Lower-left corner of rectangle to be marked, and its width 

and height. 

 

inset_ax : `.Axes` 

An optional inset axes to draw connecting lines to. Two lines are 

drawn connecting the indicator box to the inset axes on corners 

chosen so as to not overlap with the indicator box. 

 

transform : `.Transform` 

Transform for the rectangle co-ordinates. Defaults to 

`ax.transAxes`, i.e. the units of *rect* are in axes-relative 

coordinates. 

 

facecolor : Matplotlib color 

Facecolor of the rectangle (default 'none'). 

 

edgecolor : Matplotlib color 

Color of the rectangle and color of the connecting lines. Default 

is '0.5'. 

 

alpha : number 

Transparency of the rectangle and connector lines. Default is 0.5. 

 

zorder : number 

Drawing order of the rectangle and connector lines. Default is 4.99 

(just below the default level of inset axes). 

 

**kwargs 

Other *kwargs* are passed on to the rectangle patch. 

 

Returns 

------- 

 

rectangle_patch: `.Patches.Rectangle` 

Rectangle artist. 

 

connector_lines: 4-tuple of `.Patches.ConnectionPatch` 

One for each of four connector lines. Two are set with visibility 

to *False*, but the user can set the visibility to True if the 

automatic choice is not deemed correct. 

 

""" 

 

# to make the axes connectors work, we need to apply the aspect to 

# the parent axes. 

self.apply_aspect() 

 

if transform is None: 

transform = self.transData 

label = kwargs.pop('label', 'indicate_inset') 

 

xy = (bounds[0], bounds[1]) 

rectpatch = mpatches.Rectangle(xy, bounds[2], bounds[3], 

facecolor=facecolor, edgecolor=edgecolor, alpha=alpha, 

zorder=zorder, label=label, transform=transform, **kwargs) 

self.add_patch(rectpatch) 

 

if inset_ax is not None: 

# want to connect the indicator to the rect.... 

 

pos = inset_ax.get_position() # this is in fig-fraction. 

coordsA = 'axes fraction' 

connects = [] 

xr = [bounds[0], bounds[0]+bounds[2]] 

yr = [bounds[1], bounds[1]+bounds[3]] 

for xc in range(2): 

for yc in range(2): 

xyA = (xc, yc) 

xyB = (xr[xc], yr[yc]) 

connects += [mpatches.ConnectionPatch(xyA, xyB, 

'axes fraction', 'data', 

axesA=inset_ax, axesB=self, arrowstyle="-", 

zorder=zorder, edgecolor=edgecolor, alpha=alpha)] 

self.add_patch(connects[-1]) 

# decide which two of the lines to keep visible.... 

pos = inset_ax.get_position() 

bboxins = pos.transformed(self.figure.transFigure) 

rectbbox = mtransforms.Bbox.from_bounds( 

*bounds).transformed(transform) 

x0 = rectbbox.x0 < bboxins.x0 

x1 = rectbbox.x1 < bboxins.x1 

y0 = rectbbox.y0 < bboxins.y0 

y1 = rectbbox.y1 < bboxins.y1 

connects[0].set_visible(x0 ^ y0) 

connects[1].set_visible(x0 == y1) 

connects[2].set_visible(x1 == y0) 

connects[3].set_visible(x1 ^ y1) 

 

return rectpatch, connects 

 

def indicate_inset_zoom(self, inset_ax, **kwargs): 

""" 

Add an inset indicator rectangle to the axes based on the axis 

limits for an *inset_ax* and draw connectors between *inset_ax* 

and the rectangle. 

 

Warnings 

-------- 

 

This method is experimental as of 3.0, and the API may change. 

 

Parameters 

---------- 

 

inset_ax : `.Axes` 

Inset axes to draw connecting lines to. Two lines are 

drawn connecting the indicator box to the inset axes on corners 

chosen so as to not overlap with the indicator box. 

 

**kwargs 

Other *kwargs* are passed on to `.Axes.inset_rectangle` 

 

Returns 

------- 

 

rectangle_patch: `.Patches.Rectangle` 

Rectangle artist. 

 

connector_lines: 4-tuple of `.Patches.ConnectionPatch` 

One for each of four connector lines. Two are set with visibility 

to *False*, but the user can set the visibility to True if the 

automatic choice is not deemed correct. 

 

""" 

 

xlim = inset_ax.get_xlim() 

ylim = inset_ax.get_ylim() 

rect = [xlim[0], ylim[0], xlim[1] - xlim[0], ylim[1] - ylim[0]] 

rectpatch, connects = self.indicate_inset( 

rect, inset_ax, **kwargs) 

 

return rectpatch, connects 

 

def text(self, x, y, s, fontdict=None, withdash=False, **kwargs): 

""" 

Add text to the axes. 

 

Add the text *s* to the axes at location *x*, *y* in data coordinates. 

 

Parameters 

---------- 

x, y : scalars 

The position to place the text. By default, this is in data 

coordinates. The coordinate system can be changed using the 

*transform* parameter. 

 

s : str 

The text. 

 

fontdict : dictionary, optional, default: None 

A dictionary to override the default text properties. If fontdict 

is None, the defaults are determined by your rc parameters. 

 

withdash : boolean, optional, default: False 

Creates a `~matplotlib.text.TextWithDash` instance instead of a 

`~matplotlib.text.Text` instance. 

 

Returns 

------- 

text : `.Text` 

The created `.Text` instance. 

 

Other Parameters 

---------------- 

**kwargs : `~matplotlib.text.Text` properties. 

Other miscellaneous text parameters. 

 

Examples 

-------- 

Individual keyword arguments can be used to override any given 

parameter:: 

 

>>> text(x, y, s, fontsize=12) 

 

The default transform specifies that text is in data coords, 

alternatively, you can specify text in axis coords (0,0 is 

lower-left and 1,1 is upper-right). The example below places 

text in the center of the axes:: 

 

>>> text(0.5, 0.5, 'matplotlib', horizontalalignment='center', 

... verticalalignment='center', transform=ax.transAxes) 

 

You can put a rectangular box around the text instance (e.g., to 

set a background color) by using the keyword `bbox`. `bbox` is 

a dictionary of `~matplotlib.patches.Rectangle` 

properties. For example:: 

 

>>> text(x, y, s, bbox=dict(facecolor='red', alpha=0.5)) 

""" 

default = { 

'verticalalignment': 'baseline', 

'horizontalalignment': 'left', 

'transform': self.transData, 

'clip_on': False} 

 

# At some point if we feel confident that TextWithDash 

# is robust as a drop-in replacement for Text and that 

# the performance impact of the heavier-weight class 

# isn't too significant, it may make sense to eliminate 

# the withdash kwarg and simply delegate whether there's 

# a dash to TextWithDash and dashlength. 

if withdash: 

t = mtext.TextWithDash( 

x=x, y=y, text=s) 

else: 

t = mtext.Text( 

x=x, y=y, text=s) 

 

t.update(default) 

if fontdict is not None: 

t.update(fontdict) 

t.update(kwargs) 

 

t.set_clip_path(self.patch) 

self._add_text(t) 

return t 

 

@docstring.dedent_interpd 

def annotate(self, s, xy, *args, **kwargs): 

a = mtext.Annotation(s, xy, *args, **kwargs) 

a.set_transform(mtransforms.IdentityTransform()) 

if 'clip_on' in kwargs: 

a.set_clip_path(self.patch) 

self._add_text(a) 

return a 

annotate.__doc__ = mtext.Annotation.__init__.__doc__ 

#### Lines and spans 

 

@docstring.dedent_interpd 

def axhline(self, y=0, xmin=0, xmax=1, **kwargs): 

""" 

Add a horizontal line across the axis. 

 

Parameters 

---------- 

y : scalar, optional, default: 0 

y position in data coordinates of the horizontal line. 

 

xmin : scalar, optional, default: 0 

Should be between 0 and 1, 0 being the far left of the plot, 1 the 

far right of the plot. 

 

xmax : scalar, optional, default: 1 

Should be between 0 and 1, 0 being the far left of the plot, 1 the 

far right of the plot. 

 

Returns 

------- 

line : :class:`~matplotlib.lines.Line2D` 

 

Other Parameters 

---------------- 

**kwargs : 

Valid kwargs are :class:`~matplotlib.lines.Line2D` properties, 

with the exception of 'transform': 

 

%(Line2D)s 

 

See also 

-------- 

hlines : Add horizontal lines in data coordinates. 

axhspan : Add a horizontal span (rectangle) across the axis. 

 

Examples 

-------- 

 

* draw a thick red hline at 'y' = 0 that spans the xrange:: 

 

>>> axhline(linewidth=4, color='r') 

 

* draw a default hline at 'y' = 1 that spans the xrange:: 

 

>>> axhline(y=1) 

 

* draw a default hline at 'y' = .5 that spans the middle half of 

the xrange:: 

 

>>> axhline(y=.5, xmin=0.25, xmax=0.75) 

 

""" 

if "transform" in kwargs: 

raise ValueError( 

"'transform' is not allowed as a kwarg;" 

+ "axhline generates its own transform.") 

ymin, ymax = self.get_ybound() 

 

# We need to strip away the units for comparison with 

# non-unitized bounds 

self._process_unit_info(ydata=y, kwargs=kwargs) 

yy = self.convert_yunits(y) 

scaley = (yy < ymin) or (yy > ymax) 

 

trans = self.get_yaxis_transform(which='grid') 

l = mlines.Line2D([xmin, xmax], [y, y], transform=trans, **kwargs) 

self.add_line(l) 

self.autoscale_view(scalex=False, scaley=scaley) 

return l 

 

@docstring.dedent_interpd 

def axvline(self, x=0, ymin=0, ymax=1, **kwargs): 

""" 

Add a vertical line across the axes. 

 

Parameters 

---------- 

x : scalar, optional, default: 0 

x position in data coordinates of the vertical line. 

 

ymin : scalar, optional, default: 0 

Should be between 0 and 1, 0 being the bottom of the plot, 1 the 

top of the plot. 

 

ymax : scalar, optional, default: 1 

Should be between 0 and 1, 0 being the bottom of the plot, 1 the 

top of the plot. 

 

Returns 

------- 

line : :class:`~matplotlib.lines.Line2D` 

 

Other Parameters 

---------------- 

**kwargs : 

Valid kwargs are :class:`~matplotlib.lines.Line2D` properties, 

with the exception of 'transform': 

 

%(Line2D)s 

 

Examples 

-------- 

* draw a thick red vline at *x* = 0 that spans the yrange:: 

 

>>> axvline(linewidth=4, color='r') 

 

* draw a default vline at *x* = 1 that spans the yrange:: 

 

>>> axvline(x=1) 

 

* draw a default vline at *x* = .5 that spans the middle half of 

the yrange:: 

 

>>> axvline(x=.5, ymin=0.25, ymax=0.75) 

 

See also 

-------- 

vlines : Add vertical lines in data coordinates. 

axvspan : Add a vertical span (rectangle) across the axis. 

""" 

 

if "transform" in kwargs: 

raise ValueError( 

"'transform' is not allowed as a kwarg;" 

+ "axvline generates its own transform.") 

xmin, xmax = self.get_xbound() 

 

# We need to strip away the units for comparison with 

# non-unitized bounds 

self._process_unit_info(xdata=x, kwargs=kwargs) 

xx = self.convert_xunits(x) 

scalex = (xx < xmin) or (xx > xmax) 

 

trans = self.get_xaxis_transform(which='grid') 

l = mlines.Line2D([x, x], [ymin, ymax], transform=trans, **kwargs) 

self.add_line(l) 

self.autoscale_view(scalex=scalex, scaley=False) 

return l 

 

@docstring.dedent_interpd 

def axhspan(self, ymin, ymax, xmin=0, xmax=1, **kwargs): 

""" 

Add a horizontal span (rectangle) across the axis. 

 

Draw a horizontal span (rectangle) from *ymin* to *ymax*. 

With the default values of *xmin* = 0 and *xmax* = 1, this 

always spans the xrange, regardless of the xlim settings, even 

if you change them, e.g., with the :meth:`set_xlim` command. 

That is, the horizontal extent is in axes coords: 0=left, 

0.5=middle, 1.0=right but the *y* location is in data 

coordinates. 

 

Parameters 

---------- 

ymin : float 

Lower limit of the horizontal span in data units. 

ymax : float 

Upper limit of the horizontal span in data units. 

xmin : float, optional, default: 0 

Lower limit of the vertical span in axes (relative 

0-1) units. 

xmax : float, optional, default: 1 

Upper limit of the vertical span in axes (relative 

0-1) units. 

 

Returns 

------- 

Polygon : `~matplotlib.patches.Polygon` 

 

Other Parameters 

---------------- 

**kwargs : `~matplotlib.patches.Polygon` properties. 

 

%(Polygon)s 

 

See Also 

-------- 

axvspan : Add a vertical span across the axes. 

""" 

trans = self.get_yaxis_transform(which='grid') 

 

# process the unit information 

self._process_unit_info([xmin, xmax], [ymin, ymax], kwargs=kwargs) 

 

# first we need to strip away the units 

xmin, xmax = self.convert_xunits([xmin, xmax]) 

ymin, ymax = self.convert_yunits([ymin, ymax]) 

 

verts = (xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin) 

p = mpatches.Polygon(verts, **kwargs) 

p.set_transform(trans) 

self.add_patch(p) 

self.autoscale_view(scalex=False) 

return p 

 

def axvspan(self, xmin, xmax, ymin=0, ymax=1, **kwargs): 

""" 

Add a vertical span (rectangle) across the axes. 

 

Draw a vertical span (rectangle) from `xmin` to `xmax`. With 

the default values of `ymin` = 0 and `ymax` = 1. This always 

spans the yrange, regardless of the ylim settings, even if you 

change them, e.g., with the :meth:`set_ylim` command. That is, 

the vertical extent is in axes coords: 0=bottom, 0.5=middle, 

1.0=top but the x location is in data coordinates. 

 

Parameters 

---------- 

xmin : scalar 

Number indicating the first X-axis coordinate of the vertical 

span rectangle in data units. 

xmax : scalar 

Number indicating the second X-axis coordinate of the vertical 

span rectangle in data units. 

ymin : scalar, optional 

Number indicating the first Y-axis coordinate of the vertical 

span rectangle in relative Y-axis units (0-1). Default to 0. 

ymax : scalar, optional 

Number indicating the second Y-axis coordinate of the vertical 

span rectangle in relative Y-axis units (0-1). Default to 1. 

 

Returns 

------- 

rectangle : matplotlib.patches.Polygon 

Vertical span (rectangle) from (xmin, ymin) to (xmax, ymax). 

 

Other Parameters 

---------------- 

**kwargs 

Optional parameters are properties of the class 

matplotlib.patches.Polygon. 

 

See Also 

-------- 

axhspan : Add a horizontal span across the axes. 

 

Examples 

-------- 

Draw a vertical, green, translucent rectangle from x = 1.25 to 

x = 1.55 that spans the yrange of the axes. 

 

>>> axvspan(1.25, 1.55, facecolor='g', alpha=0.5) 

 

""" 

trans = self.get_xaxis_transform(which='grid') 

 

# process the unit information 

self._process_unit_info([xmin, xmax], [ymin, ymax], kwargs=kwargs) 

 

# first we need to strip away the units 

xmin, xmax = self.convert_xunits([xmin, xmax]) 

ymin, ymax = self.convert_yunits([ymin, ymax]) 

 

verts = [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)] 

p = mpatches.Polygon(verts, **kwargs) 

p.set_transform(trans) 

self.add_patch(p) 

self.autoscale_view(scaley=False) 

return p 

 

@_preprocess_data(replace_names=["y", "xmin", "xmax", "colors"], 

label_namer="y") 

def hlines(self, y, xmin, xmax, colors='k', linestyles='solid', 

label='', **kwargs): 

""" 

Plot horizontal lines at each *y* from *xmin* to *xmax*. 

 

Parameters 

---------- 

y : scalar or sequence of scalar 

y-indexes where to plot the lines. 

 

xmin, xmax : scalar or 1D array_like 

Respective beginning and end of each line. If scalars are 

provided, all lines will have same length. 

 

colors : array_like of colors, optional, default: 'k' 

 

linestyles : {'solid', 'dashed', 'dashdot', 'dotted'}, optional 

 

label : string, optional, default: '' 

 

Returns 

------- 

lines : `~matplotlib.collections.LineCollection` 

 

Other Parameters 

---------------- 

**kwargs : `~matplotlib.collections.LineCollection` properties. 

 

See also 

-------- 

vlines : vertical lines 

axhline: horizontal line across the axes 

""" 

 

# We do the conversion first since not all unitized data is uniform 

# process the unit information 

self._process_unit_info([xmin, xmax], y, kwargs=kwargs) 

y = self.convert_yunits(y) 

xmin = self.convert_xunits(xmin) 

xmax = self.convert_xunits(xmax) 

 

if not iterable(y): 

y = [y] 

if not iterable(xmin): 

xmin = [xmin] 

if not iterable(xmax): 

xmax = [xmax] 

 

y, xmin, xmax = cbook.delete_masked_points(y, xmin, xmax) 

 

y = np.ravel(y) 

xmin = np.resize(xmin, y.shape) 

xmax = np.resize(xmax, y.shape) 

 

verts = [((thisxmin, thisy), (thisxmax, thisy)) 

for thisxmin, thisxmax, thisy in zip(xmin, xmax, y)] 

lines = mcoll.LineCollection(verts, colors=colors, 

linestyles=linestyles, label=label) 

self.add_collection(lines, autolim=False) 

lines.update(kwargs) 

 

if len(y) > 0: 

minx = min(xmin.min(), xmax.min()) 

maxx = max(xmin.max(), xmax.max()) 

miny = y.min() 

maxy = y.max() 

 

corners = (minx, miny), (maxx, maxy) 

 

self.update_datalim(corners) 

self.autoscale_view() 

 

return lines 

 

@_preprocess_data(replace_names=["x", "ymin", "ymax", "colors"], 

label_namer="x") 

def vlines(self, x, ymin, ymax, colors='k', linestyles='solid', 

label='', **kwargs): 

""" 

Plot vertical lines. 

 

Plot vertical lines at each *x* from *ymin* to *ymax*. 

 

Parameters 

---------- 

x : scalar or 1D array_like 

x-indexes where to plot the lines. 

 

ymin, ymax : scalar or 1D array_like 

Respective beginning and end of each line. If scalars are 

provided, all lines will have same length. 

 

colors : array_like of colors, optional, default: 'k' 

 

linestyles : {'solid', 'dashed', 'dashdot', 'dotted'}, optional 

 

label : string, optional, default: '' 

 

Returns 

------- 

lines : `~matplotlib.collections.LineCollection` 

 

Other Parameters 

---------------- 

**kwargs : `~matplotlib.collections.LineCollection` properties. 

 

See also 

-------- 

hlines : horizontal lines 

axvline: vertical line across the axes 

""" 

 

self._process_unit_info(xdata=x, ydata=[ymin, ymax], kwargs=kwargs) 

 

# We do the conversion first since not all unitized data is uniform 

x = self.convert_xunits(x) 

ymin = self.convert_yunits(ymin) 

ymax = self.convert_yunits(ymax) 

 

if not iterable(x): 

x = [x] 

if not iterable(ymin): 

ymin = [ymin] 

if not iterable(ymax): 

ymax = [ymax] 

 

x, ymin, ymax = cbook.delete_masked_points(x, ymin, ymax) 

 

x = np.ravel(x) 

ymin = np.resize(ymin, x.shape) 

ymax = np.resize(ymax, x.shape) 

 

verts = [((thisx, thisymin), (thisx, thisymax)) 

for thisx, thisymin, thisymax in zip(x, ymin, ymax)] 

lines = mcoll.LineCollection(verts, colors=colors, 

linestyles=linestyles, label=label) 

self.add_collection(lines, autolim=False) 

lines.update(kwargs) 

 

if len(x) > 0: 

minx = x.min() 

maxx = x.max() 

miny = min(ymin.min(), ymax.min()) 

maxy = max(ymin.max(), ymax.max()) 

 

corners = (minx, miny), (maxx, maxy) 

self.update_datalim(corners) 

self.autoscale_view() 

 

return lines 

 

@_preprocess_data(replace_names=["positions", "lineoffsets", 

"linelengths", "linewidths", 

"colors", "linestyles"], 

label_namer=None) 

@docstring.dedent_interpd 

def eventplot(self, positions, orientation='horizontal', lineoffsets=1, 

linelengths=1, linewidths=None, colors=None, 

linestyles='solid', **kwargs): 

""" 

Plot identical parallel lines at the given positions. 

 

*positions* should be a 1D or 2D array-like object, with each row 

corresponding to a row or column of lines. 

 

This type of plot is commonly used in neuroscience for representing 

neural events, where it is usually called a spike raster, dot raster, 

or raster plot. 

 

However, it is useful in any situation where you wish to show the 

timing or position of multiple sets of discrete events, such as the 

arrival times of people to a business on each day of the month or the 

date of hurricanes each year of the last century. 

 

Parameters 

---------- 

positions : 1D or 2D array-like object 

Each value is an event. If *positions* is a 2D array-like, each 

row corresponds to a row or a column of lines (depending on the 

*orientation* parameter). 

 

orientation : {'horizontal', 'vertical'}, optional 

Controls the direction of the event collections: 

 

- 'horizontal' : the lines are arranged horizontally in rows, 

and are vertical. 

- 'vertical' : the lines are arranged vertically in columns, 

and are horizontal. 

 

lineoffsets : scalar or sequence of scalars, optional, default: 1 

The offset of the center of the lines from the origin, in the 

direction orthogonal to *orientation*. 

 

linelengths : scalar or sequence of scalars, optional, default: 1 

The total height of the lines (i.e. the lines stretches from 

``lineoffset - linelength/2`` to ``lineoffset + linelength/2``). 

 

linewidths : scalar, scalar sequence or None, optional, default: None 

The line width(s) of the event lines, in points. If it is None, 

defaults to its rcParams setting. 

 

colors : color, sequence of colors or None, optional, default: None 

The color(s) of the event lines. If it is None, defaults to its 

rcParams setting. 

 

linestyles : str or tuple or a sequence of such values, optional 

Default is 'solid'. Valid strings are ['solid', 'dashed', 

'dashdot', 'dotted', '-', '--', '-.', ':']. Dash tuples 

should be of the form:: 

 

(offset, onoffseq), 

 

where *onoffseq* is an even length tuple of on and off ink 

in points. 

 

**kwargs : optional 

Other keyword arguments are line collection properties. See 

:class:`~matplotlib.collections.LineCollection` for a list of 

the valid properties. 

 

Returns 

------- 

 

list : A list of :class:`~.collections.EventCollection` objects. 

Contains the :class:`~.collections.EventCollection` that 

were added. 

 

Notes 

----- 

 

For *linelengths*, *linewidths*, *colors*, and *linestyles*, if only 

a single value is given, that value is applied to all lines. If an 

array-like is given, it must have the same length as *positions*, and 

each value will be applied to the corresponding row of the array. 

 

Examples 

-------- 

 

.. plot:: gallery/lines_bars_and_markers/eventplot_demo.py 

""" 

self._process_unit_info(xdata=positions, 

ydata=[lineoffsets, linelengths], 

kwargs=kwargs) 

 

# We do the conversion first since not all unitized data is uniform 

positions = self.convert_xunits(positions) 

lineoffsets = self.convert_yunits(lineoffsets) 

linelengths = self.convert_yunits(linelengths) 

 

if not iterable(positions): 

positions = [positions] 

elif any(iterable(position) for position in positions): 

positions = [np.asanyarray(position) for position in positions] 

else: 

positions = [np.asanyarray(positions)] 

 

if len(positions) == 0: 

return [] 

 

# prevent 'singular' keys from **kwargs dict from overriding the effect 

# of 'plural' keyword arguments (e.g. 'color' overriding 'colors') 

colors = cbook.local_over_kwdict(colors, kwargs, 'color') 

linewidths = cbook.local_over_kwdict(linewidths, kwargs, 'linewidth') 

linestyles = cbook.local_over_kwdict(linestyles, kwargs, 'linestyle') 

 

if not iterable(lineoffsets): 

lineoffsets = [lineoffsets] 

if not iterable(linelengths): 

linelengths = [linelengths] 

if not iterable(linewidths): 

linewidths = [linewidths] 

if not iterable(colors): 

colors = [colors] 

if hasattr(linestyles, 'lower') or not iterable(linestyles): 

linestyles = [linestyles] 

 

lineoffsets = np.asarray(lineoffsets) 

linelengths = np.asarray(linelengths) 

linewidths = np.asarray(linewidths) 

 

if len(lineoffsets) == 0: 

lineoffsets = [None] 

if len(linelengths) == 0: 

linelengths = [None] 

if len(linewidths) == 0: 

lineoffsets = [None] 

if len(linewidths) == 0: 

lineoffsets = [None] 

if len(colors) == 0: 

colors = [None] 

try: 

# Early conversion of the colors into RGBA values to take care 

# of cases like colors='0.5' or colors='C1'. (Issue #8193) 

colors = mcolors.to_rgba_array(colors) 

except ValueError: 

# Will fail if any element of *colors* is None. But as long 

# as len(colors) == 1 or len(positions), the rest of the 

# code should process *colors* properly. 

pass 

 

if len(lineoffsets) == 1 and len(positions) != 1: 

lineoffsets = np.tile(lineoffsets, len(positions)) 

lineoffsets[0] = 0 

lineoffsets = np.cumsum(lineoffsets) 

if len(linelengths) == 1: 

linelengths = np.tile(linelengths, len(positions)) 

if len(linewidths) == 1: 

linewidths = np.tile(linewidths, len(positions)) 

if len(colors) == 1: 

colors = list(colors) 

colors = colors * len(positions) 

if len(linestyles) == 1: 

linestyles = [linestyles] * len(positions) 

 

if len(lineoffsets) != len(positions): 

raise ValueError('lineoffsets and positions are unequal sized ' 

'sequences') 

if len(linelengths) != len(positions): 

raise ValueError('linelengths and positions are unequal sized ' 

'sequences') 

if len(linewidths) != len(positions): 

raise ValueError('linewidths and positions are unequal sized ' 

'sequences') 

if len(colors) != len(positions): 

raise ValueError('colors and positions are unequal sized ' 

'sequences') 

if len(linestyles) != len(positions): 

raise ValueError('linestyles and positions are unequal sized ' 

'sequences') 

 

colls = [] 

for position, lineoffset, linelength, linewidth, color, linestyle in \ 

zip(positions, lineoffsets, linelengths, linewidths, 

colors, linestyles): 

coll = mcoll.EventCollection(position, 

orientation=orientation, 

lineoffset=lineoffset, 

linelength=linelength, 

linewidth=linewidth, 

color=color, 

linestyle=linestyle) 

self.add_collection(coll, autolim=False) 

coll.update(kwargs) 

colls.append(coll) 

 

if len(positions) > 0: 

# try to get min/max 

min_max = [(np.min(_p), np.max(_p)) for _p in positions 

if len(_p) > 0] 

# if we have any non-empty positions, try to autoscale 

if len(min_max) > 0: 

mins, maxes = zip(*min_max) 

minpos = np.min(mins) 

maxpos = np.max(maxes) 

 

minline = (lineoffsets - linelengths).min() 

maxline = (lineoffsets + linelengths).max() 

 

if (orientation is not None and 

orientation.lower() == "vertical"): 

corners = (minline, minpos), (maxline, maxpos) 

else: # "horizontal", None or "none" (see EventCollection) 

corners = (minpos, minline), (maxpos, maxline) 

self.update_datalim(corners) 

self.autoscale_view() 

 

return colls 

 

# ### Basic plotting 

# The label_naming happens in `matplotlib.axes._base._plot_args` 

@_preprocess_data(replace_names=["x", "y"], 

positional_parameter_names=_plot_args_replacer, 

label_namer=None) 

@docstring.dedent_interpd 

def plot(self, *args, scalex=True, scaley=True, **kwargs): 

""" 

Plot y versus x as lines and/or markers. 

 

Call signatures:: 

 

plot([x], y, [fmt], data=None, **kwargs) 

plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs) 

 

The coordinates of the points or line nodes are given by *x*, *y*. 

 

The optional parameter *fmt* is a convenient way for defining basic 

formatting like color, marker and linestyle. It's a shortcut string 

notation described in the *Notes* section below. 

 

>>> plot(x, y) # plot x and y using default line style and color 

>>> plot(x, y, 'bo') # plot x and y using blue circle markers 

>>> plot(y) # plot y using x as index array 0..N-1 

>>> plot(y, 'r+') # ditto, but with red plusses 

 

You can use `.Line2D` properties as keyword arguments for more 

control on the appearance. Line properties and *fmt* can be mixed. 

The following two calls yield identical results: 

 

>>> plot(x, y, 'go--', linewidth=2, markersize=12) 

>>> plot(x, y, color='green', marker='o', linestyle='dashed', 

... linewidth=2, markersize=12) 

 

When conflicting with *fmt*, keyword arguments take precedence. 

 

**Plotting labelled data** 

 

There's a convenient way for plotting objects with labelled data (i.e. 

data that can be accessed by index ``obj['y']``). Instead of giving 

the data in *x* and *y*, you can provide the object in the *data* 

parameter and just give the labels for *x* and *y*:: 

 

>>> plot('xlabel', 'ylabel', data=obj) 

 

All indexable objects are supported. This could e.g. be a `dict`, a 

`pandas.DataFame` or a structured numpy array. 

 

 

**Plotting multiple sets of data** 

 

There are various ways to plot multiple sets of data. 

 

- The most straight forward way is just to call `plot` multiple times. 

Example: 

 

>>> plot(x1, y1, 'bo') 

>>> plot(x2, y2, 'go') 

 

- Alternatively, if your data is already a 2d array, you can pass it 

directly to *x*, *y*. A separate data set will be drawn for every 

column. 

 

Example: an array ``a`` where the first column represents the *x* 

values and the other columns are the *y* columns:: 

 

>>> plot(a[0], a[1:]) 

 

- The third way is to specify multiple sets of *[x]*, *y*, *[fmt]* 

groups:: 

 

>>> plot(x1, y1, 'g^', x2, y2, 'g-') 

 

In this case, any additional keyword argument applies to all 

datasets. Also this syntax cannot be combined with the *data* 

parameter. 

 

By default, each line is assigned a different style specified by a 

'style cycle'. The *fmt* and line property parameters are only 

necessary if you want explicit deviations from these defaults. 

Alternatively, you can also change the style cycle using the 

'axes.prop_cycle' rcParam. 

 

Parameters 

---------- 

x, y : array-like or scalar 

The horizontal / vertical coordinates of the data points. 

*x* values are optional. If not given, they default to 

``[0, ..., N-1]``. 

 

Commonly, these parameters are arrays of length N. However, 

scalars are supported as well (equivalent to an array with 

constant value). 

 

The parameters can also be 2-dimensional. Then, the columns 

represent separate data sets. 

 

fmt : str, optional 

A format string, e.g. 'ro' for red circles. See the *Notes* 

section for a full description of the format strings. 

 

Format strings are just an abbreviation for quickly setting 

basic line properties. All of these and more can also be 

controlled by keyword arguments. 

 

data : indexable object, optional 

An object with labelled data. If given, provide the label names to 

plot in *x* and *y*. 

 

.. note:: 

Technically there's a slight ambiguity in calls where the 

second label is a valid *fmt*. `plot('n', 'o', data=obj)` 

could be `plt(x, y)` or `plt(y, fmt)`. In such cases, 

the former interpretation is chosen, but a warning is issued. 

You may suppress the warning by adding an empty format string 

`plot('n', 'o', '', data=obj)`. 

 

 

Other Parameters 

---------------- 

scalex, scaley : bool, optional, default: True 

These parameters determined if the view limits are adapted to 

the data limits. The values are passed on to `autoscale_view`. 

 

**kwargs : `.Line2D` properties, optional 

*kwargs* are used to specify properties like a line label (for 

auto legends), linewidth, antialiasing, marker face color. 

Example:: 

 

>>> plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2) 

>>> plot([1,2,3], [1,4,9], 'rs', label='line 2') 

 

If you make multiple lines with one plot command, the kwargs 

apply to all those lines. 

 

Here is a list of available `.Line2D` properties: 

 

%(Line2D)s 

 

Returns 

------- 

lines 

A list of `.Line2D` objects representing the plotted data. 

 

 

See Also 

-------- 

scatter : XY scatter plot with markers of varying size and/or color ( 

sometimes also called bubble chart). 

 

 

Notes 

----- 

**Format Strings** 

 

A format string consists of a part for color, marker and line:: 

 

fmt = '[color][marker][line]' 

 

Each of them is optional. If not provided, the value from the style 

cycle is used. Exception: If ``line`` is given, but no ``marker``, 

the data will be a line without markers. 

 

**Colors** 

 

The following color abbreviations are supported: 

 

============= =============================== 

character color 

============= =============================== 

``'b'`` blue 

``'g'`` green 

``'r'`` red 

``'c'`` cyan 

``'m'`` magenta 

``'y'`` yellow 

``'k'`` black 

``'w'`` white 

============= =============================== 

 

If the color is the only part of the format string, you can 

additionally use any `matplotlib.colors` spec, e.g. full names 

(``'green'``) or hex strings (``'#008000'``). 

 

**Markers** 

 

============= =============================== 

character description 

============= =============================== 

``'.'`` point marker 

``','`` pixel marker 

``'o'`` circle marker 

``'v'`` triangle_down marker 

``'^'`` triangle_up marker 

``'<'`` triangle_left marker 

``'>'`` triangle_right marker 

``'1'`` tri_down marker 

``'2'`` tri_up marker 

``'3'`` tri_left marker 

``'4'`` tri_right marker 

``'s'`` square marker 

``'p'`` pentagon marker 

``'*'`` star marker 

``'h'`` hexagon1 marker 

``'H'`` hexagon2 marker 

``'+'`` plus marker 

``'x'`` x marker 

``'D'`` diamond marker 

``'d'`` thin_diamond marker 

``'|'`` vline marker 

``'_'`` hline marker 

============= =============================== 

 

**Line Styles** 

 

============= =============================== 

character description 

============= =============================== 

``'-'`` solid line style 

``'--'`` dashed line style 

``'-.'`` dash-dot line style 

``':'`` dotted line style 

============= =============================== 

 

Example format strings:: 

 

'b' # blue markers with default shape 

'ro' # red circles 

'g-' # green solid line 

'--' # dashed line with default color 

'k^:' # black triangle_up markers connected by a dotted line 

 

""" 

lines = [] 

 

kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D._alias_map) 

 

for line in self._get_lines(*args, **kwargs): 

self.add_line(line) 

lines.append(line) 

 

self.autoscale_view(scalex=scalex, scaley=scaley) 

return lines 

 

@_preprocess_data(replace_names=["x", "y"], label_namer="y") 

@docstring.dedent_interpd 

def plot_date(self, x, y, fmt='o', tz=None, xdate=True, ydate=False, 

**kwargs): 

""" 

Plot data that contains dates. 

 

Similar to `.plot`, this plots *y* vs. *x* as lines or markers. 

However, the axis labels are formatted as dates depending on *xdate* 

and *ydate*. 

 

Parameters 

---------- 

x, y : array-like 

The coordinates of the data points. If *xdate* or *ydate* is 

*True*, the respective values *x* or *y* are interpreted as 

:ref:`Matplotlib dates <date-format>`. 

 

fmt : str, optional 

The plot format string. For details, see the corresponding 

parameter in `.plot`. 

 

tz : [ *None* | timezone string | :class:`tzinfo` instance] 

The time zone to use in labeling dates. If *None*, defaults to 

rcParam ``timezone``. 

 

xdate : bool, optional, default: True 

If *True*, the *x*-axis will be interpreted as Matplotlib dates. 

 

ydate : bool, optional, default: False 

If *True*, the *y*-axis will be interpreted as Matplotlib dates. 

 

 

Returns 

------- 

lines 

A list of `~.Line2D` objects representing the plotted data. 

 

 

Other Parameters 

---------------- 

**kwargs 

Keyword arguments control the :class:`~matplotlib.lines.Line2D` 

properties: 

 

%(Line2D)s 

 

 

See Also 

-------- 

matplotlib.dates : Helper functions on dates. 

matplotlib.dates.date2num : Convert dates to num. 

matplotlib.dates.num2date : Convert num to dates. 

matplotlib.dates.drange : Create an equally spaced sequence of dates. 

 

 

Notes 

----- 

If you are using custom date tickers and formatters, it may be 

necessary to set the formatters/locators after the call to 

`.plot_date`. `.plot_date` will set the default tick locator to 

`.AutoDateLocator` (if the tick locator is not already set to a 

`.DateLocator` instance) and the default tick formatter to 

`.AutoDateFormatter` (if the tick formatter is not already set to a 

`.DateFormatter` instance). 

""" 

if xdate: 

self.xaxis_date(tz) 

if ydate: 

self.yaxis_date(tz) 

 

ret = self.plot(x, y, fmt, **kwargs) 

 

self.autoscale_view() 

 

return ret 

 

# @_preprocess_data() # let 'plot' do the unpacking.. 

@docstring.dedent_interpd 

def loglog(self, *args, **kwargs): 

""" 

Make a plot with log scaling on both the x and y axis. 

 

Call signatures:: 

 

loglog([x], y, [fmt], data=None, **kwargs) 

loglog([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs) 

 

This is just a thin wrapper around `.plot` which additionally changes 

both the x-axis and the y-axis to log scaling. All of the concepts and 

parameters of plot can be used here as well. 

 

The additional parameters *basex/y*, *subsx/y* and *nonposx/y* control 

the x/y-axis properties. They are just forwarded to `.Axes.set_xscale` 

and `.Axes.set_yscale`. 

 

Parameters 

---------- 

basex, basey : scalar, optional, default 10 

Base of the x/y logarithm. 

 

subsx, subsy : sequence, optional 

The location of the minor x/y ticks. If *None*, reasonable 

locations are automatically chosen depending on the number of 

decades in the plot. 

See `.Axes.set_xscale` / `.Axes.set_yscale` for details. 

 

nonposx, nonposy : {'mask', 'clip'}, optional, default 'mask' 

Non-positive values in x or y can be masked as invalid, or clipped 

to a very small positive number. 

 

Returns 

------- 

lines 

A list of `~.Line2D` objects representing the plotted data. 

 

Other Parameters 

---------------- 

**kwargs 

All parameters supported by `.plot`. 

""" 

dx = {k: kwargs.pop(k) for k in ['basex', 'subsx', 'nonposx'] 

if k in kwargs} 

dy = {k: kwargs.pop(k) for k in ['basey', 'subsy', 'nonposy'] 

if k in kwargs} 

 

self.set_xscale('log', **dx) 

self.set_yscale('log', **dy) 

 

l = self.plot(*args, **kwargs) 

return l 

 

# @_preprocess_data() # let 'plot' do the unpacking.. 

@docstring.dedent_interpd 

def semilogx(self, *args, **kwargs): 

""" 

Make a plot with log scaling on the x axis. 

 

Call signatures:: 

 

semilogx([x], y, [fmt], data=None, **kwargs) 

semilogx([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs) 

 

This is just a thin wrapper around `.plot` which additionally changes 

the x-axis to log scaling. All of the concepts and parameters of plot 

can be used here as well. 

 

The additional parameters *basex*, *subsx* and *nonposx* control the 

x-axis properties. They are just forwarded to `.Axes.set_xscale`. 

 

Parameters 

---------- 

basex : scalar, optional, default 10 

Base of the x logarithm. 

 

subsx : array_like, optional 

The location of the minor xticks. If *None*, reasonable locations 

are automatically chosen depending on the number of decades in the 

plot. See `.Axes.set_xscale` for details. 

 

nonposx : {'mask', 'clip'}, optional, default 'mask' 

Non-positive values in x can be masked as invalid, or clipped to a 

very small positive number. 

 

Returns 

------- 

lines 

A list of `~.Line2D` objects representing the plotted data. 

 

Other Parameters 

---------------- 

**kwargs 

All parameters supported by `.plot`. 

""" 

d = {k: kwargs.pop(k) for k in ['basex', 'subsx', 'nonposx'] 

if k in kwargs} 

 

self.set_xscale('log', **d) 

l = self.plot(*args, **kwargs) 

return l 

 

# @_preprocess_data() # let 'plot' do the unpacking.. 

@docstring.dedent_interpd 

def semilogy(self, *args, **kwargs): 

""" 

Make a plot with log scaling on the y axis. 

 

Call signatures:: 

 

semilogy([x], y, [fmt], data=None, **kwargs) 

semilogy([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs) 

 

This is just a thin wrapper around `.plot` which additionally changes 

the y-axis to log scaling. All of the concepts and parameters of plot 

can be used here as well. 

 

The additional parameters *basey*, *subsy* and *nonposy* control the 

y-axis properties. They are just forwarded to `.Axes.set_yscale`. 

 

Parameters 

---------- 

basey : scalar, optional, default 10 

Base of the y logarithm. 

 

subsy : array_like, optional 

The location of the minor yticks. If *None*, reasonable locations 

are automatically chosen depending on the number of decades in the 

plot. See `.Axes.set_yscale` for details. 

 

nonposy : {'mask', 'clip'}, optional, default 'mask' 

Non-positive values in y can be masked as invalid, or clipped to a 

very small positive number. 

 

Returns 

------- 

lines 

A list of `~.Line2D` objects representing the plotted data. 

 

Other Parameters 

---------------- 

**kwargs 

All parameters supported by `.plot`. 

""" 

d = {k: kwargs.pop(k) for k in ['basey', 'subsy', 'nonposy'] 

if k in kwargs} 

self.set_yscale('log', **d) 

l = self.plot(*args, **kwargs) 

 

return l 

 

@_preprocess_data(replace_names=["x"], label_namer="x") 

def acorr(self, x, **kwargs): 

""" 

Plot the autocorrelation of *x*. 

 

Parameters 

---------- 

 

x : sequence of scalar 

 

detrend : callable, optional, default: `mlab.detrend_none` 

*x* is detrended by the *detrend* callable. Default is no 

normalization. 

 

normed : bool, optional, default: True 

If ``True``, input vectors are normalised to unit length. 

 

usevlines : bool, optional, default: True 

If ``True``, `Axes.vlines` is used to plot the vertical lines from 

the origin to the acorr. Otherwise, `Axes.plot` is used. 

 

maxlags : int, optional, default: 10 

Number of lags to show. If ``None``, will return all 

``2 * len(x) - 1`` lags. 

 

Returns 

------- 

lags : array (length ``2*maxlags+1``) 

lag vector. 

c : array (length ``2*maxlags+1``) 

auto correlation vector. 

line : `.LineCollection` or `.Line2D` 

`.Artist` added to the axes of the correlation. 

 

`.LineCollection` if *usevlines* is True 

`.Line2D` if *usevlines* is False 

b : `.Line2D` or None 

Horizontal line at 0 if *usevlines* is True 

None *usevlines* is False 

 

Other Parameters 

---------------- 

linestyle : `.Line2D` property, optional, default: None 

Only used if usevlines is ``False``. 

 

marker : str, optional, default: 'o' 

 

Notes 

----- 

The cross correlation is performed with :func:`numpy.correlate` with 

``mode = 2``. 

""" 

return self.xcorr(x, x, **kwargs) 

 

@_preprocess_data(replace_names=["x", "y"], label_namer="y") 

def xcorr(self, x, y, normed=True, detrend=mlab.detrend_none, 

usevlines=True, maxlags=10, **kwargs): 

r""" 

Plot the cross correlation between *x* and *y*. 

 

The correlation with lag k is defined as 

:math:`\sum_n x[n+k] \cdot y^*[n]`, where :math:`y^*` is the complex 

conjugate of :math:`y`. 

 

Parameters 

---------- 

x : sequence of scalars of length n 

 

y : sequence of scalars of length n 

 

detrend : callable, optional, default: `mlab.detrend_none` 

*x* is detrended by the *detrend* callable. Default is no 

normalization. 

 

normed : bool, optional, default: True 

If ``True``, input vectors are normalised to unit length. 

 

usevlines : bool, optional, default: True 

If ``True``, `Axes.vlines` is used to plot the vertical lines from 

the origin to the acorr. Otherwise, `Axes.plot` is used. 

 

maxlags : int, optional 

Number of lags to show. If None, will return all ``2 * len(x) - 1`` 

lags. Default is 10. 

 

Returns 

------- 

lags : array (length ``2*maxlags+1``) 

lag vector. 

c : array (length ``2*maxlags+1``) 

auto correlation vector. 

line : `.LineCollection` or `.Line2D` 

`.Artist` added to the axes of the correlation 

 

`.LineCollection` if *usevlines* is True 

`.Line2D` if *usevlines* is False 

b : `.Line2D` or None 

Horizontal line at 0 if *usevlines* is True 

None *usevlines* is False 

 

Other Parameters 

---------------- 

linestyle : `.Line2D` property, optional 

Only used if usevlines is ``False``. 

 

marker : string, optional 

Default is 'o'. 

 

Notes 

----- 

The cross correlation is performed with :func:`numpy.correlate` with 

``mode = 2``. 

""" 

Nx = len(x) 

if Nx != len(y): 

raise ValueError('x and y must be equal length') 

 

x = detrend(np.asarray(x)) 

y = detrend(np.asarray(y)) 

 

correls = np.correlate(x, y, mode=2) 

 

if normed: 

correls /= np.sqrt(np.dot(x, x) * np.dot(y, y)) 

 

if maxlags is None: 

maxlags = Nx - 1 

 

if maxlags >= Nx or maxlags < 1: 

raise ValueError('maxlags must be None or strictly ' 

'positive < %d' % Nx) 

 

lags = np.arange(-maxlags, maxlags + 1) 

correls = correls[Nx - 1 - maxlags:Nx + maxlags] 

 

if usevlines: 

a = self.vlines(lags, [0], correls, **kwargs) 

# Make label empty so only vertical lines get a legend entry 

kwargs.pop('label', '') 

b = self.axhline(**kwargs) 

else: 

kwargs.setdefault('marker', 'o') 

kwargs.setdefault('linestyle', 'None') 

a, = self.plot(lags, correls, **kwargs) 

b = None 

return lags, correls, a, b 

 

#### Specialized plotting 

 

@_preprocess_data(replace_names=["x", "y"], label_namer="y") 

def step(self, x, y, *args, where='pre', **kwargs): 

""" 

Make a step plot. 

 

Call signatures:: 

 

step(x, y, [fmt], *, data=None, where='pre', **kwargs) 

step(x, y, [fmt], x2, y2, [fmt2], ..., *, where='pre', **kwargs) 

 

This is just a thin wrapper around `.plot` which changes some 

formatting options. Most of the concepts and parameters of plot can be 

used here as well. 

 

Parameters 

---------- 

x : array_like 

1-D sequence of x positions. It is assumed, but not checked, that 

it is uniformly increasing. 

 

y : array_like 

1-D sequence of y levels. 

 

fmt : str, optional 

A format string, e.g. 'g' for a green line. See `.plot` for a more 

detailed description. 

 

Note: While full format strings are accepted, it is recommended to 

only specify the color. Line styles are currently ignored (use 

the keyword argument *linestyle* instead). Markers are accepted 

and plotted on the given positions, however, this is a rarely 

needed feature for step plots. 

 

data : indexable object, optional 

An object with labelled data. If given, provide the label names to 

plot in *x* and *y*. 

 

where : {'pre', 'post', 'mid'}, optional, default 'pre' 

Define where the steps should be placed: 

 

- 'pre': The y value is continued constantly to the left from 

every *x* position, i.e. the interval ``(x[i-1], x[i]]`` has the 

value ``y[i]``. 

- 'post': The y value is continued constantly to the right from 

every *x* position, i.e. the interval ``[x[i], x[i+1])`` has the 

value ``y[i]``. 

- 'mid': Steps occur half-way between the *x* positions. 

 

Returns 

------- 

lines 

A list of `.Line2D` objects representing the plotted data. 

 

Other Parameters 

---------------- 

**kwargs 

Additional parameters are the same as those for `.plot`. 

 

Notes 

----- 

.. [notes section required to get data note injection right] 

""" 

if where not in ('pre', 'post', 'mid'): 

raise ValueError("'where' argument to step must be " 

"'pre', 'post' or 'mid'") 

kwargs['linestyle'] = 'steps-' + where + kwargs.get('linestyle', '') 

 

return self.plot(x, y, *args, **kwargs) 

 

@_preprocess_data(replace_names=["x", "left", 

"height", "width", 

"y", "bottom", 

"color", "edgecolor", "linewidth", 

"tick_label", "xerr", "yerr", 

"ecolor"], 

label_namer=None, 

replace_all_args=True 

) 

@docstring.dedent_interpd 

def bar(self, x, height, width=0.8, bottom=None, *, align="center", 

**kwargs): 

r""" 

Make a bar plot. 

 

The bars are positioned at *x* with the given *align*\ment. Their 

dimensions are given by *width* and *height*. The vertical baseline 

is *bottom* (default 0). 

 

Each of *x*, *height*, *width*, and *bottom* may either be a scalar 

applying to all bars, or it may be a sequence of length N providing a 

separate value for each bar. 

 

Parameters 

---------- 

x : sequence of scalars 

The x coordinates of the bars. See also *align* for the 

alignment of the bars to the coordinates. 

 

height : scalar or sequence of scalars 

The height(s) of the bars. 

 

width : scalar or array-like, optional 

The width(s) of the bars (default: 0.8). 

 

bottom : scalar or array-like, optional 

The y coordinate(s) of the bars bases (default: 0). 

 

align : {'center', 'edge'}, optional, default: 'center' 

Alignment of the bars to the *x* coordinates: 

 

- 'center': Center the base on the *x* positions. 

- 'edge': Align the left edges of the bars with the *x* positions. 

 

To align the bars on the right edge pass a negative *width* and 

``align='edge'``. 

 

Returns 

------- 

container : `.BarContainer` 

Container with all the bars and optionally errorbars. 

 

Other Parameters 

---------------- 

color : scalar or array-like, optional 

The colors of the bar faces. 

 

edgecolor : scalar or array-like, optional 

The colors of the bar edges. 

 

linewidth : scalar or array-like, optional 

Width of the bar edge(s). If 0, don't draw edges. 

 

tick_label : string or array-like, optional 

The tick labels of the bars. 

Default: None (Use default numeric labels.) 

 

xerr, yerr : scalar or array-like of shape(N,) or shape(2,N), optional 

If not *None*, add horizontal / vertical errorbars to the bar tips. 

The values are +/- sizes relative to the data: 

 

- scalar: symmetric +/- values for all bars 

- shape(N,): symmetric +/- values for each bar 

- shape(2,N): Separate - and + values for each bar. First row 

contains the lower errors, the second row contains the 

upper errors. 

- *None*: No errorbar. (Default) 

 

See :doc:`/gallery/statistics/errorbar_features` 

for an example on the usage of ``xerr`` and ``yerr``. 

 

ecolor : scalar or array-like, optional, default: 'black' 

The line color of the errorbars. 

 

capsize : scalar, optional 

The length of the error bar caps in points. 

Default: None, which will take the value from 

:rc:`errorbar.capsize`. 

 

error_kw : dict, optional 

Dictionary of kwargs to be passed to the `~.Axes.errorbar` 

method. Values of *ecolor* or *capsize* defined here take 

precedence over the independent kwargs. 

 

log : bool, optional, default: False 

If *True*, set the y-axis to be log scale. 

 

orientation : {'vertical', 'horizontal'}, optional 

*This is for internal use only.* Please use `barh` for 

horizontal bar plots. Default: 'vertical'. 

 

See also 

-------- 

barh: Plot a horizontal bar plot. 

 

Notes 

----- 

The optional arguments *color*, *edgecolor*, *linewidth*, 

*xerr*, and *yerr* can be either scalars or sequences of 

length equal to the number of bars. This enables you to use 

bar as the basis for stacked bar charts, or candlestick plots. 

Detail: *xerr* and *yerr* are passed directly to 

:meth:`errorbar`, so they can also have shape 2xN for 

independent specification of lower and upper errors. 

 

Other optional kwargs: 

 

%(Rectangle)s 

 

""" 

kwargs = cbook.normalize_kwargs(kwargs, mpatches.Patch._alias_map) 

color = kwargs.pop('color', None) 

if color is None: 

color = self._get_patches_for_fill.get_next_color() 

edgecolor = kwargs.pop('edgecolor', None) 

linewidth = kwargs.pop('linewidth', None) 

 

# Because xerr and yerr will be passed to errorbar, most dimension 

# checking and processing will be left to the errorbar method. 

xerr = kwargs.pop('xerr', None) 

yerr = kwargs.pop('yerr', None) 

error_kw = kwargs.pop('error_kw', {}) 

ecolor = kwargs.pop('ecolor', 'k') 

capsize = kwargs.pop('capsize', rcParams["errorbar.capsize"]) 

error_kw.setdefault('ecolor', ecolor) 

error_kw.setdefault('capsize', capsize) 

 

orientation = kwargs.pop('orientation', 'vertical') 

log = kwargs.pop('log', False) 

label = kwargs.pop('label', '') 

tick_labels = kwargs.pop('tick_label', None) 

 

adjust_ylim = False 

adjust_xlim = False 

 

y = bottom # Matches barh call signature. 

if orientation == 'vertical': 

if bottom is None: 

if self.get_yscale() == 'log': 

adjust_ylim = True 

y = 0 

 

elif orientation == 'horizontal': 

if x is None: 

if self.get_xscale() == 'log': 

adjust_xlim = True 

x = 0 

 

if orientation == 'vertical': 

self._process_unit_info(xdata=x, ydata=height, kwargs=kwargs) 

if log: 

self.set_yscale('log', nonposy='clip') 

elif orientation == 'horizontal': 

self._process_unit_info(xdata=width, ydata=y, kwargs=kwargs) 

if log: 

self.set_xscale('log', nonposx='clip') 

else: 

raise ValueError('invalid orientation: %s' % orientation) 

 

# lets do some conversions now since some types cannot be 

# subtracted uniformly 

if self.xaxis is not None: 

x = self.convert_xunits(x) 

width = self.convert_xunits(width) 

if xerr is not None: 

xerr = self.convert_xunits(xerr) 

 

if self.yaxis is not None: 

y = self.convert_yunits(y) 

height = self.convert_yunits(height) 

if yerr is not None: 

yerr = self.convert_yunits(yerr) 

 

x, height, width, y, linewidth = np.broadcast_arrays( 

# Make args iterable too. 

np.atleast_1d(x), height, width, y, linewidth) 

 

# Now that units have been converted, set the tick locations. 

if orientation == 'vertical': 

tick_label_axis = self.xaxis 

tick_label_position = x 

elif orientation == 'horizontal': 

tick_label_axis = self.yaxis 

tick_label_position = y 

 

linewidth = itertools.cycle(np.atleast_1d(linewidth)) 

color = itertools.chain(itertools.cycle(mcolors.to_rgba_array(color)), 

# Fallback if color == "none". 

itertools.repeat('none')) 

if edgecolor is None: 

edgecolor = itertools.repeat(None) 

else: 

edgecolor = itertools.chain( 

itertools.cycle(mcolors.to_rgba_array(edgecolor)), 

# Fallback if edgecolor == "none". 

itertools.repeat('none')) 

 

# We will now resolve the alignment and really have 

# left, bottom, width, height vectors 

if align == 'center': 

if orientation == 'vertical': 

left = x - width / 2 

bottom = y 

elif orientation == 'horizontal': 

bottom = y - height / 2 

left = x 

elif align == 'edge': 

left = x 

bottom = y 

else: 

raise ValueError('invalid alignment: %s' % align) 

 

patches = [] 

args = zip(left, bottom, width, height, color, edgecolor, linewidth) 

for l, b, w, h, c, e, lw in args: 

r = mpatches.Rectangle( 

xy=(l, b), width=w, height=h, 

facecolor=c, 

edgecolor=e, 

linewidth=lw, 

label='_nolegend_', 

) 

r.update(kwargs) 

r.get_path()._interpolation_steps = 100 

if orientation == 'vertical': 

r.sticky_edges.y.append(b) 

elif orientation == 'horizontal': 

r.sticky_edges.x.append(l) 

self.add_patch(r) 

patches.append(r) 

 

if xerr is not None or yerr is not None: 

if orientation == 'vertical': 

# using list comps rather than arrays to preserve unit info 

ex = [l + 0.5 * w for l, w in zip(left, width)] 

ey = [b + h for b, h in zip(bottom, height)] 

 

elif orientation == 'horizontal': 

# using list comps rather than arrays to preserve unit info 

ex = [l + w for l, w in zip(left, width)] 

ey = [b + 0.5 * h for b, h in zip(bottom, height)] 

 

error_kw.setdefault("label", '_nolegend_') 

 

errorbar = self.errorbar(ex, ey, 

yerr=yerr, xerr=xerr, 

fmt='none', **error_kw) 

else: 

errorbar = None 

 

if adjust_xlim: 

xmin, xmax = self.dataLim.intervalx 

xmin = min(w for w in width if w > 0) 

if xerr is not None: 

xmin = xmin - np.max(xerr) 

xmin = max(xmin * 0.9, 1e-100) 

self.dataLim.intervalx = (xmin, xmax) 

 

if adjust_ylim: 

ymin, ymax = self.dataLim.intervaly 

ymin = min(h for h in height if h > 0) 

if yerr is not None: 

ymin = ymin - np.max(yerr) 

ymin = max(ymin * 0.9, 1e-100) 

self.dataLim.intervaly = (ymin, ymax) 

self.autoscale_view() 

 

bar_container = BarContainer(patches, errorbar, label=label) 

self.add_container(bar_container) 

 

if tick_labels is not None: 

tick_labels = np.broadcast_to(tick_labels, len(patches)) 

tick_label_axis.set_ticks(tick_label_position) 

tick_label_axis.set_ticklabels(tick_labels) 

 

return bar_container 

 

@docstring.dedent_interpd 

def barh(self, y, width, height=0.8, left=None, *, align="center", 

**kwargs): 

r""" 

Make a horizontal bar plot. 

 

The bars are positioned at *y* with the given *align*\ment. Their 

dimensions are given by *width* and *height*. The horizontal baseline 

is *left* (default 0). 

 

Each of *y*, *width*, *height*, and *left* may either be a scalar 

applying to all bars, or it may be a sequence of length N providing a 

separate value for each bar. 

 

Parameters 

---------- 

y : scalar or array-like 

The y coordinates of the bars. See also *align* for the 

alignment of the bars to the coordinates. 

 

width : scalar or array-like 

The width(s) of the bars. 

 

height : sequence of scalars, optional, default: 0.8 

The heights of the bars. 

 

left : sequence of scalars 

The x coordinates of the left sides of the bars (default: 0). 

 

align : {'center', 'edge'}, optional, default: 'center' 

Alignment of the base to the *y* coordinates*: 

 

- 'center': Center the bars on the *y* positions. 

- 'edge': Align the bottom edges of the bars with the *y* 

positions. 

 

To align the bars on the top edge pass a negative *height* and 

``align='edge'``. 

 

Returns 

------- 

container : `.BarContainer` 

Container with all the bars and optionally errorbars. 

 

Other Parameters 

---------------- 

color : scalar or array-like, optional 

The colors of the bar faces. 

 

edgecolor : scalar or array-like, optional 

The colors of the bar edges. 

 

linewidth : scalar or array-like, optional 

Width of the bar edge(s). If 0, don't draw edges. 

 

tick_label : string or array-like, optional 

The tick labels of the bars. 

Default: None (Use default numeric labels.) 

 

xerr, yerr : scalar or array-like of shape(N,) or shape(2,N), optional 

If not ``None``, add horizontal / vertical errorbars to the 

bar tips. The values are +/- sizes relative to the data: 

 

- scalar: symmetric +/- values for all bars 

- shape(N,): symmetric +/- values for each bar 

- shape(2,N): Separate - and + values for each bar. First row 

contains the lower errors, the second row contains the 

upper errors. 

- *None*: No errorbar. (default) 

 

See :doc:`/gallery/statistics/errorbar_features` 

for an example on the usage of ``xerr`` and ``yerr``. 

 

ecolor : scalar or array-like, optional, default: 'black' 

The line color of the errorbars. 

 

capsize : scalar, optional 

The length of the error bar caps in points. 

Default: None, which will take the value from 

:rc:`errorbar.capsize`. 

 

error_kw : dict, optional 

Dictionary of kwargs to be passed to the `~.Axes.errorbar` 

method. Values of *ecolor* or *capsize* defined here take 

precedence over the independent kwargs. 

 

log : bool, optional, default: False 

If ``True``, set the x-axis to be log scale. 

 

See also 

-------- 

bar: Plot a vertical bar plot. 

 

Notes 

----- 

The optional arguments *color*, *edgecolor*, *linewidth*, 

*xerr*, and *yerr* can be either scalars or sequences of 

length equal to the number of bars. This enables you to use 

bar as the basis for stacked bar charts, or candlestick plots. 

Detail: *xerr* and *yerr* are passed directly to 

:meth:`errorbar`, so they can also have shape 2xN for 

independent specification of lower and upper errors. 

 

Other optional kwargs: 

 

%(Rectangle)s 

 

""" 

kwargs.setdefault('orientation', 'horizontal') 

patches = self.bar(x=left, height=height, width=width, bottom=y, 

align=align, **kwargs) 

return patches 

 

@_preprocess_data(label_namer=None) 

@docstring.dedent_interpd 

def broken_barh(self, xranges, yrange, **kwargs): 

""" 

Plot a horizontal sequence of rectangles. 

 

A rectangle is drawn for each element of *xranges*. All rectangles 

have the same vertical position and size defined by *yrange*. 

 

This is a convenience function for instantiating a 

`.BrokenBarHCollection`, adding it to the axes and autoscaling the 

view. 

 

Parameters 

---------- 

xranges : sequence of tuples (*xmin*, *xwidth*) 

The x-positions and extends of the rectangles. For each tuple 

(*xmin*, *xwidth*) a rectangle is drawn from *xmin* to *xmin* + 

*xwidth*. 

yranges : (*ymin*, *ymax*) 

The y-position and extend for all the rectangles. 

 

Other Parameters 

---------------- 

**kwargs : :class:`.BrokenBarHCollection` properties 

 

Each *kwarg* can be either a single argument applying to all 

rectangles, e.g.:: 

 

facecolors='black' 

 

or a sequence of arguments over which is cycled, e.g.:: 

 

facecolors=('black', 'blue') 

 

would create interleaving black and blue rectangles. 

 

Supported keywords: 

 

%(BrokenBarHCollection)s 

 

Returns 

------- 

collection : A :class:`~.collections.BrokenBarHCollection` 

 

Notes 

----- 

.. [Notes section required for data comment. See #10189.] 

 

""" 

# process the unit information 

if len(xranges): 

xdata = cbook.safe_first_element(xranges) 

else: 

xdata = None 

if len(yrange): 

ydata = cbook.safe_first_element(yrange) 

else: 

ydata = None 

self._process_unit_info(xdata=xdata, 

ydata=ydata, 

kwargs=kwargs) 

xranges = self.convert_xunits(xranges) 

yrange = self.convert_yunits(yrange) 

 

col = mcoll.BrokenBarHCollection(xranges, yrange, **kwargs) 

self.add_collection(col, autolim=True) 

self.autoscale_view() 

 

return col 

 

@_preprocess_data(replace_all_args=True, label_namer=None) 

def stem(self, *args, linefmt=None, markerfmt=None, basefmt=None, 

bottom=0, label=None): 

""" 

Create a stem plot. 

 

A stem plot plots vertical lines at each *x* location from the baseline 

to *y*, and places a marker there. 

 

Call signature:: 

 

stem([x,] y, linefmt=None, markerfmt=None, basefmt=None) 

 

The x-positions are optional. The formats may be provided either as 

positional or as keyword-arguments. 

 

Parameters 

---------- 

x : array-like, optional 

The x-positions of the stems. Default: (0, 1, ..., len(y) - 1). 

 

y : array-like 

The y-values of the stem heads. 

 

linefmt : str, optional 

A string defining the properties of the vertical lines. Usually, 

this will be a color or a color and a linestyle: 

 

========= ============= 

Character Line Style 

========= ============= 

``'-'`` solid line 

``'--'`` dashed line 

``'-.'`` dash-dot line 

``':'`` dotted line 

========= ============= 

 

Default: 'C0-', i.e. solid line with the first color of the color 

cycle. 

 

Note: While it is technically possible to specify valid formats 

other than color or color and linestyle (e.g. 'rx' or '-.'), this 

is beyond the intention of the method and will most likely not 

result in a reasonable reasonable plot. 

 

markerfmt : str, optional 

A string defining the properties of the markers at the stem heads. 

Default: 'C0o', i.e. filled circles with the first color of the 

color cycle. 

 

basefmt : str, optional 

A format string defining the properties of the baseline. 

 

Default: 'C3-' ('C2-' in classic mode). 

 

bottom : float, optional, default: 0 

The y-position of the baseline. 

 

label : str, optional, default: None 

The label to use for the stems in legends. 

 

 

Returns 

------- 

container : :class:`~matplotlib.container.StemContainer` 

The container may be treated like a tuple 

(*markerline*, *stemlines*, *baseline*) 

 

 

Notes 

----- 

 

.. seealso:: 

The MATLAB function 

`stem <http://www.mathworks.com/help/techdoc/ref/stem.html>`_ 

which inspired this method. 

 

""" 

if not 1 <= len(args) <= 5: 

raise TypeError('stem expected between 1 and 5 positional ' 

'arguments, got {}'.format(args)) 

 

y = np.asarray(args[0]) 

args = args[1:] 

 

# Try a second one 

if not args: 

x = np.arange(len(y)) 

else: 

x = y 

y = np.asarray(args[0], dtype=float) 

args = args[1:] 

 

# defaults for formats 

if linefmt is None: 

try: 

# fallback to positional argument 

linefmt = args[0] 

except IndexError: 

linecolor = 'C0' 

linemarker = 'None' 

linestyle = '-' 

else: 

linestyle, linemarker, linecolor = \ 

_process_plot_format(linefmt) 

else: 

linestyle, linemarker, linecolor = _process_plot_format(linefmt) 

 

if markerfmt is None: 

try: 

# fallback to positional argument 

markerfmt = args[1] 

except IndexError: 

markercolor = 'C0' 

markermarker = 'o' 

markerstyle = 'None' 

else: 

markerstyle, markermarker, markercolor = \ 

_process_plot_format(markerfmt) 

else: 

markerstyle, markermarker, markercolor = \ 

_process_plot_format(markerfmt) 

 

if basefmt is None: 

try: 

# fallback to positional argument 

basefmt = args[2] 

except IndexError: 

if rcParams['_internal.classic_mode']: 

basecolor = 'C2' 

else: 

basecolor = 'C3' 

basemarker = 'None' 

basestyle = '-' 

else: 

basestyle, basemarker, basecolor = \ 

_process_plot_format(basefmt) 

else: 

basestyle, basemarker, basecolor = _process_plot_format(basefmt) 

 

markerline, = self.plot(x, y, color=markercolor, linestyle=markerstyle, 

marker=markermarker, label="_nolegend_") 

 

stemlines = [] 

for thisx, thisy in zip(x, y): 

l, = self.plot([thisx, thisx], [bottom, thisy], 

color=linecolor, linestyle=linestyle, 

marker=linemarker, label="_nolegend_") 

stemlines.append(l) 

 

baseline, = self.plot([np.min(x), np.max(x)], [bottom, bottom], 

color=basecolor, linestyle=basestyle, 

marker=basemarker, label="_nolegend_") 

 

stem_container = StemContainer((markerline, stemlines, baseline), 

label=label) 

self.add_container(stem_container) 

 

return stem_container 

 

@_preprocess_data(replace_names=["x", "explode", "labels", "colors"], 

label_namer=None) 

def pie(self, 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): 

""" 

Plot a pie chart. 

 

Make a pie chart of array *x*. The fractional area of each wedge is 

given by ``x/sum(x)``. If ``sum(x) < 1``, then the values of *x* give 

the fractional area directly and the array will not be normalized. The 

resulting pie will have an empty wedge of size ``1 - sum(x)``. 

 

The wedges are plotted counterclockwise, by default starting from the 

x-axis. 

 

Parameters 

---------- 

x : array-like 

The wedge sizes. 

 

explode : array-like, optional, default: None 

If not *None*, is a ``len(x)`` array which specifies the fraction 

of the radius with which to offset each wedge. 

 

labels : list, optional, default: None 

A sequence of strings providing the labels for each wedge 

 

colors : array-like, optional, default: None 

A sequence of matplotlib color args through which the pie chart 

will cycle. If *None*, will use the colors in the currently 

active cycle. 

 

autopct : None (default), string, or function, optional 

If not *None*, is a string or function used to label the wedges 

with their numeric value. The label will be placed inside the 

wedge. If it is a format string, the label will be ``fmt%pct``. 

If it is a function, it will be called. 

 

pctdistance : float, optional, default: 0.6 

The ratio between the center of each pie slice and the start of 

the text generated by *autopct*. Ignored if *autopct* is *None*. 

 

shadow : bool, optional, default: False 

Draw a shadow beneath the pie. 

 

labeldistance : float, optional, default: 1.1 

The radial distance at which the pie labels are drawn 

 

startangle : float, optional, default: None 

If not *None*, rotates the start of the pie chart by *angle* 

degrees counterclockwise from the x-axis. 

 

radius : float, optional, default: None 

The radius of the pie, if *radius* is *None* it will be set to 1. 

 

counterclock : bool, optional, default: True 

Specify fractions direction, clockwise or counterclockwise. 

 

wedgeprops : dict, optional, default: None 

Dict of arguments passed to the wedge objects making the pie. 

For example, you can pass in ``wedgeprops = {'linewidth': 3}`` 

to set the width of the wedge border lines equal to 3. 

For more details, look at the doc/arguments of the wedge object. 

By default ``clip_on=False``. 

 

textprops : dict, optional, default: None 

Dict of arguments to pass to the text objects. 

 

center : list of float, optional, default: (0, 0) 

Center position of the chart. Takes value (0, 0) or is a sequence 

of 2 scalars. 

 

frame : bool, optional, default: False 

Plot axes frame with the chart if true. 

 

rotatelabels : bool, optional, default: False 

Rotate each label to the angle of the corresponding slice if true. 

 

Returns 

------- 

patches : list 

A sequence of :class:`matplotlib.patches.Wedge` instances 

 

texts : list 

A list of the label :class:`matplotlib.text.Text` instances. 

 

autotexts : list 

A list of :class:`~matplotlib.text.Text` instances for the numeric 

labels. This will only be returned if the parameter *autopct* is 

not *None*. 

 

Notes 

----- 

The pie chart will probably look best if the figure and axes are 

square, or the Axes aspect is equal. 

This method sets the aspect ratio of the axis to "equal". 

The axes aspect ratio can be controlled with `Axes.set_aspect`. 

""" 

self.set_aspect('equal') 

x = np.array(x, np.float32) 

 

sx = x.sum() 

if sx > 1: 

x /= sx 

 

if labels is None: 

labels = [''] * len(x) 

if explode is None: 

explode = [0] * len(x) 

if len(x) != len(labels): 

raise ValueError("'label' must be of length 'x'") 

if len(x) != len(explode): 

raise ValueError("'explode' must be of length 'x'") 

if colors is None: 

get_next_color = self._get_patches_for_fill.get_next_color 

else: 

color_cycle = itertools.cycle(colors) 

 

def get_next_color(): 

return next(color_cycle) 

 

if radius is None: 

radius = 1 

 

# Starting theta1 is the start fraction of the circle 

if startangle is None: 

theta1 = 0 

else: 

theta1 = startangle / 360.0 

 

# set default values in wedge_prop 

if wedgeprops is None: 

wedgeprops = {} 

wedgeprops.setdefault('clip_on', False) 

 

if textprops is None: 

textprops = {} 

textprops.setdefault('clip_on', False) 

 

texts = [] 

slices = [] 

autotexts = [] 

 

i = 0 

for frac, label, expl in zip(x, labels, explode): 

x, y = center 

theta2 = (theta1 + frac) if counterclock else (theta1 - frac) 

thetam = 2 * np.pi * 0.5 * (theta1 + theta2) 

x += expl * math.cos(thetam) 

y += expl * math.sin(thetam) 

 

w = mpatches.Wedge((x, y), radius, 360. * min(theta1, theta2), 

360. * max(theta1, theta2), 

facecolor=get_next_color(), 

**wedgeprops) 

slices.append(w) 

self.add_patch(w) 

w.set_label(label) 

 

if shadow: 

# make sure to add a shadow after the call to 

# add_patch so the figure and transform props will be 

# set 

shad = mpatches.Shadow(w, -0.02, -0.02) 

shad.set_zorder(0.9 * w.get_zorder()) 

shad.set_label('_nolegend_') 

self.add_patch(shad) 

 

xt = x + labeldistance * radius * math.cos(thetam) 

yt = y + labeldistance * radius * math.sin(thetam) 

label_alignment_h = xt > 0 and 'left' or 'right' 

label_alignment_v = 'center' 

label_rotation = 'horizontal' 

if rotatelabels: 

label_alignment_v = yt > 0 and 'bottom' or 'top' 

label_rotation = np.rad2deg(thetam) + (0 if xt > 0 else 180) 

props = dict(horizontalalignment=label_alignment_h, 

verticalalignment=label_alignment_v, 

rotation=label_rotation, 

size=rcParams['xtick.labelsize']) 

props.update(textprops) 

 

t = self.text(xt, yt, label, **props) 

 

texts.append(t) 

 

if autopct is not None: 

xt = x + pctdistance * radius * math.cos(thetam) 

yt = y + pctdistance * radius * math.sin(thetam) 

if isinstance(autopct, str): 

s = autopct % (100. * frac) 

elif callable(autopct): 

s = autopct(100. * frac) 

else: 

raise TypeError( 

'autopct must be callable or a format string') 

 

props = dict(horizontalalignment='center', 

verticalalignment='center') 

props.update(textprops) 

t = self.text(xt, yt, s, **props) 

 

autotexts.append(t) 

 

theta1 = theta2 

i += 1 

 

if not frame: 

self.set_frame_on(False) 

 

self.set_xlim((-1.25 + center[0], 

1.25 + center[0])) 

self.set_ylim((-1.25 + center[1], 

1.25 + center[1])) 

self.set_xticks([]) 

self.set_yticks([]) 

 

if autopct is None: 

return slices, texts 

else: 

return slices, texts, autotexts 

 

@_preprocess_data(replace_names=["x", "y", "xerr", "yerr"], 

label_namer="y") 

@docstring.dedent_interpd 

def errorbar(self, 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, 

**kwargs): 

""" 

Plot y versus x as lines and/or markers with attached errorbars. 

 

*x*, *y* define the data locations, *xerr*, *yerr* define the errorbar 

sizes. By default, this draws the data markers/lines as well the 

errorbars. Use fmt='none' to draw errorbars without any data markers. 

 

Parameters 

---------- 

x, y : scalar or array-like 

The data positions. 

 

xerr, yerr : scalar or array-like, shape(N,) or shape(2,N), optional 

The errorbar sizes: 

 

- scalar: Symmetric +/- values for all data points. 

- shape(N,): Symmetric +/-values for each data point. 

- shape(2,N): Separate - and + values for each bar. First row 

contains the lower errors, the second row contains the 

upper errors. 

- *None*: No errorbar. 

 

Note that all error arrays should have *positive* values. 

 

See :doc:`/gallery/statistics/errorbar_features` 

for an example on the usage of ``xerr`` and ``yerr``. 

 

fmt : plot format string, optional, default: '' 

The format for the data points / data lines. See `.plot` for 

details. 

 

Use 'none' (case insensitive) to plot errorbars without any data 

markers. 

 

ecolor : mpl color, optional, default: None 

A matplotlib color arg which gives the color the errorbar lines. 

If None, use the color of the line connecting the markers. 

 

elinewidth : scalar, optional, default: None 

The linewidth of the errorbar lines. If None, the linewidth of 

the current style is used. 

 

capsize : scalar, optional, default: None 

The length of the error bar caps in points. If None, it will take 

the value from :rc:`errorbar.capsize`. 

 

capthick : scalar, optional, default: None 

An alias to the keyword argument *markeredgewidth* (a.k.a. *mew*). 

This setting is a more sensible name for the property that 

controls the thickness of the error bar cap in points. For 

backwards compatibility, if *mew* or *markeredgewidth* are given, 

then they will over-ride *capthick*. This may change in future 

releases. 

 

barsabove : bool, optional, default: False 

If True, will plot the errorbars above the plot 

symbols. Default is below. 

 

lolims, uplims, xlolims, xuplims : bool, optional, default: None 

These arguments can be used to indicate that a value gives only 

upper/lower limits. In that case a caret symbol is used to 

indicate this. *lims*-arguments may be of the same type as *xerr* 

and *yerr*. To use limits with inverted axes, :meth:`set_xlim` 

or :meth:`set_ylim` must be called before :meth:`errorbar`. 

 

errorevery : positive integer, optional, default: 1 

Subsamples the errorbars. e.g., if errorevery=5, errorbars for 

every 5-th datapoint will be plotted. The data plot itself still 

shows all data points. 

 

Returns 

------- 

container : :class:`~.container.ErrorbarContainer` 

The container contains: 

 

- plotline: :class:`~matplotlib.lines.Line2D` instance of 

x, y plot markers and/or line. 

- caplines: A tuple of :class:`~matplotlib.lines.Line2D` instances 

of the error bar caps. 

- barlinecols: A tuple of 

:class:`~matplotlib.collections.LineCollection` with the 

horizontal and vertical error ranges. 

 

Other Parameters 

---------------- 

**kwargs : 

All other keyword arguments are passed on to the plot 

command for the markers. For example, this code makes big red 

squares with thick green edges:: 

 

x,y,yerr = rand(3,10) 

errorbar(x, y, yerr, marker='s', mfc='red', 

mec='green', ms=20, mew=4) 

 

where *mfc*, *mec*, *ms* and *mew* are aliases for the longer 

property names, *markerfacecolor*, *markeredgecolor*, *markersize* 

and *markeredgewidth*. 

 

Valid kwargs for the marker properties are `.Lines2D` properties: 

 

%(Line2D)s 

 

Notes 

----- 

.. [Notes section required for data comment. See #10189.] 

 

""" 

kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D._alias_map) 

# anything that comes in as 'None', drop so the default thing 

# happens down stream 

kwargs = {k: v for k, v in kwargs.items() if v is not None} 

kwargs.setdefault('zorder', 2) 

 

if errorevery < 1: 

raise ValueError( 

'errorevery has to be a strictly positive integer') 

 

self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs) 

 

plot_line = (fmt.lower() != 'none') 

label = kwargs.pop("label", None) 

 

if fmt == '': 

fmt_style_kwargs = {} 

else: 

fmt_style_kwargs = {k: v for k, v in 

zip(('linestyle', 'marker', 'color'), 

_process_plot_format(fmt)) 

if v is not None} 

if fmt == 'none': 

# Remove alpha=0 color that _process_plot_format returns 

fmt_style_kwargs.pop('color') 

 

if ('color' in kwargs or 'color' in fmt_style_kwargs or 

ecolor is not None): 

base_style = {} 

if 'color' in kwargs: 

base_style['color'] = kwargs.pop('color') 

else: 

base_style = next(self._get_lines.prop_cycler) 

 

base_style['label'] = '_nolegend_' 

base_style.update(fmt_style_kwargs) 

if 'color' not in base_style: 

base_style['color'] = 'C0' 

if ecolor is None: 

ecolor = base_style['color'] 

# make sure all the args are iterable; use lists not arrays to 

# preserve units 

if not iterable(x): 

x = [x] 

 

if not iterable(y): 

y = [y] 

 

if xerr is not None: 

if not iterable(xerr): 

xerr = [xerr] * len(x) 

 

if yerr is not None: 

if not iterable(yerr): 

yerr = [yerr] * len(y) 

 

# make the style dict for the 'normal' plot line 

plot_line_style = { 

**base_style, 

**kwargs, 

'zorder': (kwargs['zorder'] - .1 if barsabove else 

kwargs['zorder'] + .1), 

} 

 

# make the style dict for the line collections (the bars) 

eb_lines_style = dict(base_style) 

eb_lines_style.pop('marker', None) 

eb_lines_style.pop('linestyle', None) 

eb_lines_style['color'] = ecolor 

 

if elinewidth: 

eb_lines_style['linewidth'] = elinewidth 

elif 'linewidth' in kwargs: 

eb_lines_style['linewidth'] = kwargs['linewidth'] 

 

for key in ('transform', 'alpha', 'zorder', 'rasterized'): 

if key in kwargs: 

eb_lines_style[key] = kwargs[key] 

 

# set up cap style dictionary 

eb_cap_style = dict(base_style) 

# eject any marker information from format string 

eb_cap_style.pop('marker', None) 

eb_lines_style.pop('markerfacecolor', None) 

eb_lines_style.pop('markeredgewidth', None) 

eb_lines_style.pop('markeredgecolor', None) 

eb_cap_style.pop('ls', None) 

eb_cap_style['linestyle'] = 'none' 

if capsize is None: 

capsize = rcParams["errorbar.capsize"] 

if capsize > 0: 

eb_cap_style['markersize'] = 2. * capsize 

if capthick is not None: 

eb_cap_style['markeredgewidth'] = capthick 

 

# For backwards-compat, allow explicit setting of 

# 'markeredgewidth' to over-ride capthick. 

for key in ('markeredgewidth', 'transform', 'alpha', 

'zorder', 'rasterized'): 

if key in kwargs: 

eb_cap_style[key] = kwargs[key] 

eb_cap_style['color'] = ecolor 

 

data_line = None 

if plot_line: 

data_line = mlines.Line2D(x, y, **plot_line_style) 

self.add_line(data_line) 

 

barcols = [] 

caplines = [] 

 

# arrays fine here, they are booleans and hence not units 

lolims = np.broadcast_to(lolims, len(x)).astype(bool) 

uplims = np.broadcast_to(uplims, len(x)).astype(bool) 

xlolims = np.broadcast_to(xlolims, len(x)).astype(bool) 

xuplims = np.broadcast_to(xuplims, len(x)).astype(bool) 

 

everymask = np.arange(len(x)) % errorevery == 0 

 

def xywhere(xs, ys, mask): 

""" 

return xs[mask], ys[mask] where mask is True but xs and 

ys are not arrays 

""" 

assert len(xs) == len(ys) 

assert len(xs) == len(mask) 

xs = [thisx for thisx, b in zip(xs, mask) if b] 

ys = [thisy for thisy, b in zip(ys, mask) if b] 

return xs, ys 

 

def extract_err(err, data): 

'''private function to compute error bars 

 

Parameters 

---------- 

err : iterable 

xerr or yerr from errorbar 

data : iterable 

x or y from errorbar 

''' 

try: 

a, b = err 

except (TypeError, ValueError): 

pass 

else: 

if iterable(a) and iterable(b): 

# using list comps rather than arrays to preserve units 

low = [thisx - thiserr for thisx, thiserr 

in cbook.safezip(data, a)] 

high = [thisx + thiserr for thisx, thiserr 

in cbook.safezip(data, b)] 

return low, high 

# Check if xerr is scalar or symmetric. Asymmetric is handled 

# above. This prevents Nx2 arrays from accidentally 

# being accepted, when the user meant the 2xN transpose. 

# special case for empty lists 

if len(err) > 1: 

fe = safe_first_element(err) 

if len(err) != len(data) or np.size(fe) > 1: 

raise ValueError("err must be [ scalar | N, Nx1 " 

"or 2xN array-like ]") 

# using list comps rather than arrays to preserve units 

low = [thisx - thiserr for thisx, thiserr 

in cbook.safezip(data, err)] 

high = [thisx + thiserr for thisx, thiserr 

in cbook.safezip(data, err)] 

return low, high 

 

if xerr is not None: 

left, right = extract_err(xerr, x) 

# select points without upper/lower limits in x and 

# draw normal errorbars for these points 

noxlims = ~(xlolims | xuplims) 

if noxlims.any() or len(noxlims) == 0: 

yo, _ = xywhere(y, right, noxlims & everymask) 

lo, ro = xywhere(left, right, noxlims & everymask) 

barcols.append(self.hlines(yo, lo, ro, **eb_lines_style)) 

if capsize > 0: 

caplines.append(mlines.Line2D(lo, yo, marker='|', 

**eb_cap_style)) 

caplines.append(mlines.Line2D(ro, yo, marker='|', 

**eb_cap_style)) 

 

if xlolims.any(): 

yo, _ = xywhere(y, right, xlolims & everymask) 

lo, ro = xywhere(x, right, xlolims & everymask) 

barcols.append(self.hlines(yo, lo, ro, **eb_lines_style)) 

rightup, yup = xywhere(right, y, xlolims & everymask) 

if self.xaxis_inverted(): 

marker = mlines.CARETLEFTBASE 

else: 

marker = mlines.CARETRIGHTBASE 

caplines.append( 

mlines.Line2D(rightup, yup, ls='None', marker=marker, 

**eb_cap_style)) 

if capsize > 0: 

xlo, ylo = xywhere(x, y, xlolims & everymask) 

caplines.append(mlines.Line2D(xlo, ylo, marker='|', 

**eb_cap_style)) 

 

if xuplims.any(): 

yo, _ = xywhere(y, right, xuplims & everymask) 

lo, ro = xywhere(left, x, xuplims & everymask) 

barcols.append(self.hlines(yo, lo, ro, **eb_lines_style)) 

leftlo, ylo = xywhere(left, y, xuplims & everymask) 

if self.xaxis_inverted(): 

marker = mlines.CARETRIGHTBASE 

else: 

marker = mlines.CARETLEFTBASE 

caplines.append( 

mlines.Line2D(leftlo, ylo, ls='None', marker=marker, 

**eb_cap_style)) 

if capsize > 0: 

xup, yup = xywhere(x, y, xuplims & everymask) 

caplines.append(mlines.Line2D(xup, yup, marker='|', 

**eb_cap_style)) 

 

if yerr is not None: 

lower, upper = extract_err(yerr, y) 

# select points without upper/lower limits in y and 

# draw normal errorbars for these points 

noylims = ~(lolims | uplims) 

if noylims.any() or len(noylims) == 0: 

xo, _ = xywhere(x, lower, noylims & everymask) 

lo, uo = xywhere(lower, upper, noylims & everymask) 

barcols.append(self.vlines(xo, lo, uo, **eb_lines_style)) 

if capsize > 0: 

caplines.append(mlines.Line2D(xo, lo, marker='_', 

**eb_cap_style)) 

caplines.append(mlines.Line2D(xo, uo, marker='_', 

**eb_cap_style)) 

 

if lolims.any(): 

xo, _ = xywhere(x, lower, lolims & everymask) 

lo, uo = xywhere(y, upper, lolims & everymask) 

barcols.append(self.vlines(xo, lo, uo, **eb_lines_style)) 

xup, upperup = xywhere(x, upper, lolims & everymask) 

if self.yaxis_inverted(): 

marker = mlines.CARETDOWNBASE 

else: 

marker = mlines.CARETUPBASE 

caplines.append( 

mlines.Line2D(xup, upperup, ls='None', marker=marker, 

**eb_cap_style)) 

if capsize > 0: 

xlo, ylo = xywhere(x, y, lolims & everymask) 

caplines.append(mlines.Line2D(xlo, ylo, marker='_', 

**eb_cap_style)) 

 

if uplims.any(): 

xo, _ = xywhere(x, lower, uplims & everymask) 

lo, uo = xywhere(lower, y, uplims & everymask) 

barcols.append(self.vlines(xo, lo, uo, **eb_lines_style)) 

xlo, lowerlo = xywhere(x, lower, uplims & everymask) 

if self.yaxis_inverted(): 

marker = mlines.CARETUPBASE 

else: 

marker = mlines.CARETDOWNBASE 

caplines.append( 

mlines.Line2D(xlo, lowerlo, ls='None', marker=marker, 

**eb_cap_style)) 

if capsize > 0: 

xup, yup = xywhere(x, y, uplims & everymask) 

caplines.append(mlines.Line2D(xup, yup, marker='_', 

**eb_cap_style)) 

for l in caplines: 

self.add_line(l) 

 

self.autoscale_view() 

errorbar_container = ErrorbarContainer((data_line, tuple(caplines), 

tuple(barcols)), 

has_xerr=(xerr is not None), 

has_yerr=(yerr is not None), 

label=label) 

self.containers.append(errorbar_container) 

 

return errorbar_container # (l0, caplines, barcols) 

 

@_preprocess_data(label_namer=None) 

def boxplot(self, 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): 

""" 

Make a box and whisker plot. 

 

Make a box and whisker plot for each column of ``x`` or each 

vector in sequence ``x``. The box extends from the lower to 

upper quartile values of the data, with a line at the median. 

The whiskers extend from the box to show the range of the 

data. Flier points are those past the end of the whiskers. 

 

Parameters 

---------- 

x : Array or a sequence of vectors. 

The input data. 

 

notch : bool, optional (False) 

If `True`, will produce a notched box plot. Otherwise, a 

rectangular boxplot is produced. The notches represent the 

confidence interval (CI) around the median. See the entry 

for the ``bootstrap`` parameter for information regarding 

how the locations of the notches are computed. 

 

.. note:: 

 

In cases where the values of the CI are less than the 

lower quartile or greater than the upper quartile, the 

notches will extend beyond the box, giving it a 

distinctive "flipped" appearance. This is expected 

behavior and consistent with other statistical 

visualization packages. 

 

sym : str, optional 

The default symbol for flier points. Enter an empty string 

('') if you don't want to show fliers. If `None`, then the 

fliers default to 'b+' If you want more control use the 

flierprops kwarg. 

 

vert : bool, optional (True) 

If `True` (default), makes the boxes vertical. If `False`, 

everything is drawn horizontally. 

 

whis : float, sequence, or string (default = 1.5) 

As a float, determines the reach of the whiskers to the beyond the 

first and third quartiles. In other words, where IQR is the 

interquartile range (`Q3-Q1`), the upper whisker will extend to 

last datum less than `Q3 + whis*IQR`). Similarly, the lower whisker 

will extend to the first datum greater than `Q1 - whis*IQR`. 

Beyond the whiskers, data 

are considered outliers and are plotted as individual 

points. Set this to an unreasonably high value to force the 

whiskers to show the min and max values. Alternatively, set 

this to an ascending sequence of percentile (e.g., [5, 95]) 

to set the whiskers at specific percentiles of the data. 

Finally, ``whis`` can be the string ``'range'`` to force the 

whiskers to the min and max of the data. 

 

bootstrap : int, optional 

Specifies whether to bootstrap the confidence intervals 

around the median for notched boxplots. If ``bootstrap`` is 

None, no bootstrapping is performed, and notches are 

calculated using a Gaussian-based asymptotic approximation 

(see McGill, R., Tukey, J.W., and Larsen, W.A., 1978, and 

Kendall and Stuart, 1967). Otherwise, bootstrap specifies 

the number of times to bootstrap the median to determine its 

95% confidence intervals. Values between 1000 and 10000 are 

recommended. 

 

usermedians : array-like, optional 

An array or sequence whose first dimension (or length) is 

compatible with ``x``. This overrides the medians computed 

by matplotlib for each element of ``usermedians`` that is not 

`None`. When an element of ``usermedians`` is None, the median 

will be computed by matplotlib as normal. 

 

conf_intervals : array-like, optional 

Array or sequence whose first dimension (or length) is 

compatible with ``x`` and whose second dimension is 2. When 

the an element of ``conf_intervals`` is not None, the 

notch locations computed by matplotlib are overridden 

(provided ``notch`` is `True`). When an element of 

``conf_intervals`` is `None`, the notches are computed by the 

method specified by the other kwargs (e.g., ``bootstrap``). 

 

positions : array-like, optional 

Sets the positions of the boxes. The ticks and limits are 

automatically set to match the positions. Defaults to 

`range(1, N+1)` where N is the number of boxes to be drawn. 

 

widths : scalar or array-like 

Sets the width of each box either with a scalar or a 

sequence. The default is 0.5, or ``0.15*(distance between 

extreme positions)``, if that is smaller. 

 

patch_artist : bool, optional (False) 

If `False` produces boxes with the Line2D artist. Otherwise, 

boxes and drawn with Patch artists. 

 

labels : sequence, optional 

Labels for each dataset. Length must be compatible with 

dimensions of ``x``. 

 

manage_xticks : bool, optional (True) 

If the function should adjust the xlim and xtick locations. 

 

autorange : bool, optional (False) 

When `True` and the data are distributed such that the 25th and 

75th percentiles are equal, ``whis`` is set to ``'range'`` such 

that the whisker ends are at the minimum and maximum of the data. 

 

meanline : bool, optional (False) 

If `True` (and ``showmeans`` is `True`), will try to render 

the mean as a line spanning the full width of the box 

according to ``meanprops`` (see below). Not recommended if 

``shownotches`` is also True. Otherwise, means will be shown 

as points. 

 

zorder : scalar, optional (None) 

Sets the zorder of the boxplot. 

 

Other Parameters 

---------------- 

showcaps : bool, optional (True) 

Show the caps on the ends of whiskers. 

showbox : bool, optional (True) 

Show the central box. 

showfliers : bool, optional (True) 

Show the outliers beyond the caps. 

showmeans : bool, optional (False) 

Show the arithmetic means. 

capprops : dict, optional (None) 

Specifies the style of the caps. 

boxprops : dict, optional (None) 

Specifies the style of the box. 

whiskerprops : dict, optional (None) 

Specifies the style of the whiskers. 

flierprops : dict, optional (None) 

Specifies the style of the fliers. 

medianprops : dict, optional (None) 

Specifies the style of the median. 

meanprops : dict, optional (None) 

Specifies the style of the mean. 

 

Returns 

------- 

result : dict 

A dictionary mapping each component of the boxplot to a list 

of the :class:`matplotlib.lines.Line2D` instances 

created. That dictionary has the following keys (assuming 

vertical boxplots): 

 

- ``boxes``: the main body of the boxplot showing the 

quartiles and the median's confidence intervals if 

enabled. 

 

- ``medians``: horizontal lines at the median of each box. 

 

- ``whiskers``: the vertical lines extending to the most 

extreme, non-outlier data points. 

 

- ``caps``: the horizontal lines at the ends of the 

whiskers. 

 

- ``fliers``: points representing data that extend beyond 

the whiskers (fliers). 

 

- ``means``: points or lines representing the means. 

 

Notes 

----- 

.. [Notes section required for data comment. See #10189.] 

 

""" 

 

# Missing arguments default to rcParams. 

if whis is None: 

whis = rcParams['boxplot.whiskers'] 

if bootstrap is None: 

bootstrap = rcParams['boxplot.bootstrap'] 

 

bxpstats = cbook.boxplot_stats(x, whis=whis, bootstrap=bootstrap, 

labels=labels, autorange=autorange) 

if notch is None: 

notch = rcParams['boxplot.notch'] 

if vert is None: 

vert = rcParams['boxplot.vertical'] 

if patch_artist is None: 

patch_artist = rcParams['boxplot.patchartist'] 

if meanline is None: 

meanline = rcParams['boxplot.meanline'] 

if showmeans is None: 

showmeans = rcParams['boxplot.showmeans'] 

if showcaps is None: 

showcaps = rcParams['boxplot.showcaps'] 

if showbox is None: 

showbox = rcParams['boxplot.showbox'] 

if showfliers is None: 

showfliers = rcParams['boxplot.showfliers'] 

 

def _update_dict(dictionary, rc_name, properties): 

""" Loads properties in the dictionary from rc file if not already 

in the dictionary""" 

rc_str = 'boxplot.{0}.{1}' 

if dictionary is None: 

dictionary = dict() 

for prop_dict in properties: 

dictionary.setdefault(prop_dict, 

rcParams[rc_str.format(rc_name, prop_dict)]) 

return dictionary 

 

# Common property dictionnaries loading from rc 

flier_props = ['color', 'marker', 'markerfacecolor', 'markeredgecolor', 

'markersize', 'linestyle', 'linewidth'] 

default_props = ['color', 'linewidth', 'linestyle'] 

 

boxprops = _update_dict(boxprops, 'boxprops', default_props) 

whiskerprops = _update_dict(whiskerprops, 'whiskerprops', 

default_props) 

capprops = _update_dict(capprops, 'capprops', default_props) 

medianprops = _update_dict(medianprops, 'medianprops', default_props) 

meanprops = _update_dict(meanprops, 'meanprops', default_props) 

flierprops = _update_dict(flierprops, 'flierprops', flier_props) 

 

if patch_artist: 

boxprops['linestyle'] = 'solid' 

boxprops['edgecolor'] = boxprops.pop('color') 

 

# if non-default sym value, put it into the flier dictionary 

# the logic for providing the default symbol ('b+') now lives 

# in bxp in the initial value of final_flierprops 

# handle all of the `sym` related logic here so we only have to pass 

# on the flierprops dict. 

if sym is not None: 

# no-flier case, which should really be done with 

# 'showfliers=False' but none-the-less deal with it to keep back 

# compatibility 

if sym == '': 

# blow away existing dict and make one for invisible markers 

flierprops = dict(linestyle='none', marker='', color='none') 

# turn the fliers off just to be safe 

showfliers = False 

# now process the symbol string 

else: 

# process the symbol string 

# discarded linestyle 

_, marker, color = _process_plot_format(sym) 

# if we have a marker, use it 

if marker is not None: 

flierprops['marker'] = marker 

# if we have a color, use it 

if color is not None: 

# assume that if color is passed in the user want 

# filled symbol, if the users want more control use 

# flierprops 

flierprops['color'] = color 

flierprops['markerfacecolor'] = color 

flierprops['markeredgecolor'] = color 

 

# replace medians if necessary: 

if usermedians is not None: 

if (len(np.ravel(usermedians)) != len(bxpstats) or 

np.shape(usermedians)[0] != len(bxpstats)): 

raise ValueError('usermedians length not compatible with x') 

else: 

# reassign medians as necessary 

for stats, med in zip(bxpstats, usermedians): 

if med is not None: 

stats['med'] = med 

 

if conf_intervals is not None: 

if np.shape(conf_intervals)[0] != len(bxpstats): 

err_mess = 'conf_intervals length not compatible with x' 

raise ValueError(err_mess) 

else: 

for stats, ci in zip(bxpstats, conf_intervals): 

if ci is not None: 

if len(ci) != 2: 

raise ValueError('each confidence interval must ' 

'have two values') 

else: 

if ci[0] is not None: 

stats['cilo'] = ci[0] 

if ci[1] is not None: 

stats['cihi'] = ci[1] 

 

artists = self.bxp(bxpstats, positions=positions, widths=widths, 

vert=vert, patch_artist=patch_artist, 

shownotches=notch, showmeans=showmeans, 

showcaps=showcaps, showbox=showbox, 

boxprops=boxprops, flierprops=flierprops, 

medianprops=medianprops, meanprops=meanprops, 

meanline=meanline, showfliers=showfliers, 

capprops=capprops, whiskerprops=whiskerprops, 

manage_xticks=manage_xticks, zorder=zorder) 

return artists 

 

def bxp(self, bxpstats, positions=None, widths=None, vert=True, 

patch_artist=False, shownotches=False, showmeans=False, 

showcaps=True, showbox=True, showfliers=True, 

boxprops=None, whiskerprops=None, flierprops=None, 

medianprops=None, capprops=None, meanprops=None, 

meanline=False, manage_xticks=True, zorder=None): 

""" 

Drawing function for box and whisker plots. 

 

Make a box and whisker plot for each column of *x* or each 

vector in sequence *x*. The box extends from the lower to 

upper quartile values of the data, with a line at the median. 

The whiskers extend from the box to show the range of the 

data. Flier points are those past the end of the whiskers. 

 

Parameters 

---------- 

 

bxpstats : list of dicts 

A list of dictionaries containing stats for each boxplot. 

Required keys are: 

 

- ``med``: The median (scalar float). 

 

- ``q1``: The first quartile (25th percentile) (scalar 

float). 

 

- ``q3``: The third quartile (75th percentile) (scalar 

float). 

 

- ``whislo``: Lower bound of the lower whisker (scalar 

float). 

 

- ``whishi``: Upper bound of the upper whisker (scalar 

float). 

 

Optional keys are: 

 

- ``mean``: The mean (scalar float). Needed if 

``showmeans=True``. 

 

- ``fliers``: Data beyond the whiskers (sequence of floats). 

Needed if ``showfliers=True``. 

 

- ``cilo`` & ``cihi``: Lower and upper confidence intervals 

about the median. Needed if ``shownotches=True``. 

 

- ``label``: Name of the dataset (string). If available, 

this will be used a tick label for the boxplot 

 

positions : array-like, default = [1, 2, ..., n] 

Sets the positions of the boxes. The ticks and limits 

are automatically set to match the positions. 

 

widths : array-like, default = None 

Either a scalar or a vector and sets the width of each 

box. The default is ``0.15*(distance between extreme 

positions)``, clipped to no less than 0.15 and no more than 

0.5. 

 

vert : bool, default = False 

If `True` (default), makes the boxes vertical. If `False`, 

makes horizontal boxes. 

 

patch_artist : bool, default = False 

If `False` produces boxes with the 

`~matplotlib.lines.Line2D` artist. If `True` produces boxes 

with the `~matplotlib.patches.Patch` artist. 

 

shownotches : bool, default = False 

If `False` (default), produces a rectangular box plot. 

If `True`, will produce a notched box plot 

 

showmeans : bool, default = False 

If `True`, will toggle on the rendering of the means 

 

showcaps : bool, default = True 

If `True`, will toggle on the rendering of the caps 

 

showbox : bool, default = True 

If `True`, will toggle on the rendering of the box 

 

showfliers : bool, default = True 

If `True`, will toggle on the rendering of the fliers 

 

boxprops : dict or None (default) 

If provided, will set the plotting style of the boxes 

 

whiskerprops : dict or None (default) 

If provided, will set the plotting style of the whiskers 

 

capprops : dict or None (default) 

If provided, will set the plotting style of the caps 

 

flierprops : dict or None (default) 

If provided will set the plotting style of the fliers 

 

medianprops : dict or None (default) 

If provided, will set the plotting style of the medians 

 

meanprops : dict or None (default) 

If provided, will set the plotting style of the means 

 

meanline : bool, default = False 

If `True` (and *showmeans* is `True`), will try to render the mean 

as a line spanning the full width of the box according to 

*meanprops*. Not recommended if *shownotches* is also True. 

Otherwise, means will be shown as points. 

 

manage_xticks : bool, default = True 

If the function should adjust the xlim and xtick locations. 

 

zorder : scalar, default = None 

The zorder of the resulting boxplot 

 

Returns 

------- 

result : dict 

A dictionary mapping each component of the boxplot to a list 

of the :class:`matplotlib.lines.Line2D` instances 

created. That dictionary has the following keys (assuming 

vertical boxplots): 

 

- ``boxes``: the main body of the boxplot showing the 

quartiles and the median's confidence intervals if 

enabled. 

 

- ``medians``: horizontal lines at the median of each box. 

 

- ``whiskers``: the vertical lines extending to the most 

extreme, non-outlier data points. 

 

- ``caps``: the horizontal lines at the ends of the 

whiskers. 

 

- ``fliers``: points representing data that extend beyond 

the whiskers (fliers). 

 

- ``means``: points or lines representing the means. 

 

Examples 

-------- 

 

.. plot:: gallery/statistics/bxp.py 

 

""" 

# lists of artists to be output 

whiskers = [] 

caps = [] 

boxes = [] 

medians = [] 

means = [] 

fliers = [] 

 

# empty list of xticklabels 

datalabels = [] 

 

# Use default zorder if none specified 

if zorder is None: 

zorder = mlines.Line2D.zorder 

 

zdelta = 0.1 

# box properties 

if patch_artist: 

final_boxprops = dict( 

linestyle=rcParams['boxplot.boxprops.linestyle'], 

edgecolor=rcParams['boxplot.boxprops.color'], 

facecolor=rcParams['patch.facecolor'], 

linewidth=rcParams['boxplot.boxprops.linewidth'] 

) 

if rcParams['_internal.classic_mode']: 

final_boxprops['facecolor'] = 'white' 

else: 

final_boxprops = dict( 

linestyle=rcParams['boxplot.boxprops.linestyle'], 

color=rcParams['boxplot.boxprops.color'], 

) 

 

final_boxprops['zorder'] = zorder 

if boxprops is not None: 

final_boxprops.update(boxprops) 

 

# other (cap, whisker) properties 

final_whiskerprops = dict( 

linestyle=rcParams['boxplot.whiskerprops.linestyle'], 

linewidth=rcParams['boxplot.whiskerprops.linewidth'], 

color=rcParams['boxplot.whiskerprops.color'], 

) 

 

final_capprops = dict( 

linestyle=rcParams['boxplot.capprops.linestyle'], 

linewidth=rcParams['boxplot.capprops.linewidth'], 

color=rcParams['boxplot.capprops.color'], 

) 

 

final_capprops['zorder'] = zorder 

if capprops is not None: 

final_capprops.update(capprops) 

 

final_whiskerprops['zorder'] = zorder 

if whiskerprops is not None: 

final_whiskerprops.update(whiskerprops) 

 

# set up the default flier properties 

final_flierprops = dict( 

linestyle=rcParams['boxplot.flierprops.linestyle'], 

linewidth=rcParams['boxplot.flierprops.linewidth'], 

color=rcParams['boxplot.flierprops.color'], 

marker=rcParams['boxplot.flierprops.marker'], 

markerfacecolor=rcParams['boxplot.flierprops.markerfacecolor'], 

markeredgecolor=rcParams['boxplot.flierprops.markeredgecolor'], 

markersize=rcParams['boxplot.flierprops.markersize'], 

) 

 

final_flierprops['zorder'] = zorder 

# flier (outlier) properties 

if flierprops is not None: 

final_flierprops.update(flierprops) 

 

# median line properties 

final_medianprops = dict( 

linestyle=rcParams['boxplot.medianprops.linestyle'], 

linewidth=rcParams['boxplot.medianprops.linewidth'], 

color=rcParams['boxplot.medianprops.color'], 

) 

final_medianprops['zorder'] = zorder + zdelta 

if medianprops is not None: 

final_medianprops.update(medianprops) 

 

# mean (line or point) properties 

if meanline: 

final_meanprops = dict( 

linestyle=rcParams['boxplot.meanprops.linestyle'], 

linewidth=rcParams['boxplot.meanprops.linewidth'], 

color=rcParams['boxplot.meanprops.color'], 

) 

else: 

final_meanprops = dict( 

linestyle='', 

marker=rcParams['boxplot.meanprops.marker'], 

markerfacecolor=rcParams['boxplot.meanprops.markerfacecolor'], 

markeredgecolor=rcParams['boxplot.meanprops.markeredgecolor'], 

markersize=rcParams['boxplot.meanprops.markersize'], 

) 

final_meanprops['zorder'] = zorder + zdelta 

if meanprops is not None: 

final_meanprops.update(meanprops) 

 

def to_vc(xs, ys): 

# convert arguments to verts and codes, append (0, 0) (ignored). 

verts = np.append(np.column_stack([xs, ys]), [(0, 0)], 0) 

codes = ([mpath.Path.MOVETO] 

+ [mpath.Path.LINETO] * (len(verts) - 2) 

+ [mpath.Path.CLOSEPOLY]) 

return verts, codes 

 

def patch_list(xs, ys, **kwargs): 

verts, codes = to_vc(xs, ys) 

path = mpath.Path(verts, codes) 

patch = mpatches.PathPatch(path, **kwargs) 

self.add_artist(patch) 

return [patch] 

 

# vertical or horizontal plot? 

if vert: 

def doplot(*args, **kwargs): 

return self.plot(*args, **kwargs) 

 

def dopatch(xs, ys, **kwargs): 

return patch_list(xs, ys, **kwargs) 

 

else: 

def doplot(*args, **kwargs): 

shuffled = [] 

for i in range(0, len(args), 2): 

shuffled.extend([args[i + 1], args[i]]) 

return self.plot(*shuffled, **kwargs) 

 

def dopatch(xs, ys, **kwargs): 

xs, ys = ys, xs # flip X, Y 

return patch_list(xs, ys, **kwargs) 

 

# input validation 

N = len(bxpstats) 

datashape_message = ("List of boxplot statistics and `{0}` " 

"values must have same the length") 

# check position 

if positions is None: 

positions = list(range(1, N + 1)) 

elif len(positions) != N: 

raise ValueError(datashape_message.format("positions")) 

 

# width 

if widths is None: 

widths = [np.clip(0.15 * np.ptp(positions), 0.15, 0.5)] * N 

elif np.isscalar(widths): 

widths = [widths] * N 

elif len(widths) != N: 

raise ValueError(datashape_message.format("widths")) 

 

for pos, width, stats in zip(positions, widths, bxpstats): 

# try to find a new label 

datalabels.append(stats.get('label', pos)) 

 

# whisker coords 

whisker_x = np.ones(2) * pos 

whiskerlo_y = np.array([stats['q1'], stats['whislo']]) 

whiskerhi_y = np.array([stats['q3'], stats['whishi']]) 

 

# cap coords 

cap_left = pos - width * 0.25 

cap_right = pos + width * 0.25 

cap_x = np.array([cap_left, cap_right]) 

cap_lo = np.ones(2) * stats['whislo'] 

cap_hi = np.ones(2) * stats['whishi'] 

 

# box and median coords 

box_left = pos - width * 0.5 

box_right = pos + width * 0.5 

med_y = [stats['med'], stats['med']] 

 

# notched boxes 

if shownotches: 

box_x = [box_left, box_right, box_right, cap_right, box_right, 

box_right, box_left, box_left, cap_left, box_left, 

box_left] 

box_y = [stats['q1'], stats['q1'], stats['cilo'], 

stats['med'], stats['cihi'], stats['q3'], 

stats['q3'], stats['cihi'], stats['med'], 

stats['cilo'], stats['q1']] 

med_x = cap_x 

 

# plain boxes 

else: 

box_x = [box_left, box_right, box_right, box_left, box_left] 

box_y = [stats['q1'], stats['q1'], stats['q3'], stats['q3'], 

stats['q1']] 

med_x = [box_left, box_right] 

 

# maybe draw the box: 

if showbox: 

if patch_artist: 

boxes.extend(dopatch(box_x, box_y, **final_boxprops)) 

else: 

boxes.extend(doplot(box_x, box_y, **final_boxprops)) 

 

# draw the whiskers 

whiskers.extend(doplot( 

whisker_x, whiskerlo_y, **final_whiskerprops 

)) 

whiskers.extend(doplot( 

whisker_x, whiskerhi_y, **final_whiskerprops 

)) 

 

# maybe draw the caps: 

if showcaps: 

caps.extend(doplot(cap_x, cap_lo, **final_capprops)) 

caps.extend(doplot(cap_x, cap_hi, **final_capprops)) 

 

# draw the medians 

medians.extend(doplot(med_x, med_y, **final_medianprops)) 

 

# maybe draw the means 

if showmeans: 

if meanline: 

means.extend(doplot( 

[box_left, box_right], [stats['mean'], stats['mean']], 

**final_meanprops 

)) 

else: 

means.extend(doplot( 

[pos], [stats['mean']], **final_meanprops 

)) 

 

# maybe draw the fliers 

if showfliers: 

# fliers coords 

flier_x = np.ones(len(stats['fliers'])) * pos 

flier_y = stats['fliers'] 

 

fliers.extend(doplot( 

flier_x, flier_y, **final_flierprops 

)) 

 

# fix our axes/ticks up a little 

if vert: 

setticks = self.set_xticks 

setlim = self.set_xlim 

setlabels = self.set_xticklabels 

else: 

setticks = self.set_yticks 

setlim = self.set_ylim 

setlabels = self.set_yticklabels 

 

if manage_xticks: 

newlimits = min(positions) - 0.5, max(positions) + 0.5 

setlim(newlimits) 

setticks(positions) 

setlabels(datalabels) 

 

return dict(whiskers=whiskers, caps=caps, boxes=boxes, 

medians=medians, fliers=fliers, means=means) 

 

@_preprocess_data(replace_names=["x", "y", "s", "linewidths", 

"edgecolors", "c", "facecolor", 

"facecolors", "color"], 

label_namer="y") 

def scatter(self, x, y, s=None, c=None, marker=None, cmap=None, norm=None, 

vmin=None, vmax=None, alpha=None, linewidths=None, 

verts=None, edgecolors=None, 

**kwargs): 

""" 

A scatter plot of *y* vs *x* with varying marker size and/or color. 

 

Parameters 

---------- 

x, y : array_like, shape (n, ) 

The data positions. 

 

s : scalar or array_like, shape (n, ), optional 

The marker size in points**2. 

Default is ``rcParams['lines.markersize'] ** 2``. 

 

c : color, sequence, or sequence of color, optional 

The marker color. Possible values: 

 

- A single color format string. 

- A sequence of color specifications of length n. 

- A sequence of n numbers to be mapped to colors using *cmap* and 

*norm*. 

- A 2-D array in which the rows are RGB or RGBA. 

 

Note that *c* should not be a single numeric RGB or RGBA sequence 

because that is indistinguishable from an array of values to be 

colormapped. If you want to specify the same RGB or RGBA value for 

all points, use a 2-D array with a single row. Otherwise, value- 

matching will have precedence in case of a size matching with *x* 

and *y*. 

 

Defaults to ``None``. In that case the marker color is determined 

by the value of ``color``, ``facecolor`` or ``facecolors``. In case 

those are not specified or ``None``, the marker color is determined 

by the next color of the ``Axes``' current "shape and fill" color 

cycle. This cycle defaults to :rc:`axes.prop_cycle`. 

 

marker : `~matplotlib.markers.MarkerStyle`, optional 

The marker style. *marker* can be either an instance of the class 

or the text shorthand for a particular marker. 

Defaults to ``None``, in which case it takes the value of 

:rc:`scatter.marker` = 'o'. 

See `~matplotlib.markers` for more information about marker styles. 

 

cmap : `~matplotlib.colors.Colormap`, optional, default: None 

A `.Colormap` instance or registered colormap name. *cmap* is only 

used if *c* is an array of floats. If ``None``, defaults to rc 

``image.cmap``. 

 

norm : `~matplotlib.colors.Normalize`, optional, default: None 

A `.Normalize` instance is used to scale luminance data to 0, 1. 

*norm* is only used if *c* is an array of floats. If *None*, use 

the default `.colors.Normalize`. 

 

vmin, vmax : scalar, optional, default: None 

*vmin* and *vmax* are used in conjunction with *norm* to normalize 

luminance data. If None, the respective min and max of the color 

array is used. *vmin* and *vmax* are ignored if you pass a *norm* 

instance. 

 

alpha : scalar, optional, default: None 

The alpha blending value, between 0 (transparent) and 1 (opaque). 

 

linewidths : scalar or array_like, optional, default: None 

The linewidth of the marker edges. Note: The default *edgecolors* 

is 'face'. You may want to change this as well. 

If *None*, defaults to rcParams ``lines.linewidth``. 

 

edgecolors : color or sequence of color, optional, default: 'face' 

The edge color of the marker. Possible values: 

 

- 'face': The edge color will always be the same as the face color. 

- 'none': No patch boundary will be drawn. 

- A matplotib color. 

 

For non-filled markers, the *edgecolors* kwarg is ignored and 

forced to 'face' internally. 

 

Returns 

------- 

paths : `~matplotlib.collections.PathCollection` 

 

Other Parameters 

---------------- 

**kwargs : `~matplotlib.collections.Collection` properties 

 

See Also 

-------- 

plot : To plot scatter plots when markers are identical in size and 

color. 

 

Notes 

----- 

 

* The `.plot` function will be faster for scatterplots where markers 

don't vary in size or color. 

 

* Any or all of *x*, *y*, *s*, and *c* may be masked arrays, in which 

case all masks will be combined and only unmasked points will be 

plotted. 

 

* Fundamentally, scatter works with 1-D arrays; *x*, *y*, *s*, and *c* 

may be input as 2-D arrays, but within scatter they will be 

flattened. The exception is *c*, which will be flattened only if its 

size matches the size of *x* and *y*. 

 

""" 

# Process **kwargs to handle aliases, conflicts with explicit kwargs: 

facecolors = None 

edgecolors = kwargs.pop('edgecolor', edgecolors) 

fc = kwargs.pop('facecolors', None) 

fc = kwargs.pop('facecolor', fc) 

if fc is not None: 

facecolors = fc 

co = kwargs.pop('color', None) 

if co is not None: 

try: 

mcolors.to_rgba_array(co) 

except ValueError: 

raise ValueError("'color' kwarg must be an mpl color" 

" spec or sequence of color specs.\n" 

"For a sequence of values to be color-mapped," 

" use the 'c' argument instead.") 

if edgecolors is None: 

edgecolors = co 

if facecolors is None: 

facecolors = co 

if c is not None: 

raise ValueError("Supply a 'c' argument or a 'color'" 

" kwarg but not both; they differ but" 

" their functionalities overlap.") 

if c is None: 

if facecolors is not None: 

c = facecolors 

else: 

if rcParams['_internal.classic_mode']: 

c = 'b' # The original default 

else: 

c = self._get_patches_for_fill.get_next_color() 

c_none = True 

else: 

c_none = False 

 

if edgecolors is None and not rcParams['_internal.classic_mode']: 

edgecolors = 'face' 

 

self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs) 

x = self.convert_xunits(x) 

y = self.convert_yunits(y) 

 

# np.ma.ravel yields an ndarray, not a masked array, 

# unless its argument is a masked array. 

xy_shape = (np.shape(x), np.shape(y)) 

x = np.ma.ravel(x) 

y = np.ma.ravel(y) 

if x.size != y.size: 

raise ValueError("x and y must be the same size") 

 

if s is None: 

if rcParams['_internal.classic_mode']: 

s = 20 

else: 

s = rcParams['lines.markersize'] ** 2.0 

 

s = np.ma.ravel(s) # This doesn't have to match x, y in size. 

 

# After this block, c_array will be None unless 

# c is an array for mapping. The potential ambiguity 

# with a sequence of 3 or 4 numbers is resolved in 

# favor of mapping, not rgb or rgba. 

 

# Convenience vars to track shape mismatch *and* conversion failures. 

valid_shape = True # will be put to the test! 

n_elem = -1 # used only for (some) exceptions 

 

if (c_none or 

co is not None or 

isinstance(c, str) or 

(isinstance(c, collections.Iterable) and 

len(c) > 0 and 

isinstance(cbook.safe_first_element(c), str))): 

c_array = None 

else: 

try: # First, does 'c' look suitable for value-mapping? 

c_array = np.asanyarray(c, dtype=float) 

n_elem = c_array.shape[0] 

if c_array.shape in xy_shape: 

c = np.ma.ravel(c_array) 

else: 

if c_array.shape in ((3,), (4,)): 

_log.warning( 

"'c' argument looks like a single numeric RGB or " 

"RGBA sequence, which should be avoided as value-" 

"mapping will have precedence in case its length " 

"matches with 'x' & 'y'. Please use a 2-D array " 

"with a single row if you really want to specify " 

"the same RGB or RGBA value for all points.") 

# Wrong size; it must not be intended for mapping. 

valid_shape = False 

c_array = None 

except ValueError: 

# Failed to make a floating-point array; c must be color specs. 

c_array = None 

 

if c_array is None: 

try: # Then is 'c' acceptable as PathCollection facecolors? 

colors = mcolors.to_rgba_array(c) 

n_elem = colors.shape[0] 

if colors.shape[0] not in (0, 1, x.size, y.size): 

# NB: remember that a single color is also acceptable. 

# Besides *colors* will be an empty array if c == 'none'. 

valid_shape = False 

raise ValueError 

except ValueError: 

if not valid_shape: # but at least one conversion succeeded. 

raise ValueError( 

"'c' argument has {nc} elements, which is not " 

"acceptable for use with 'x' with size {xs}, " 

"'y' with size {ys}." 

.format(nc=n_elem, xs=x.size, ys=y.size) 

) 

# Both the mapping *and* the RGBA conversion failed: pretty 

# severe failure => one may appreciate a verbose feedback. 

raise ValueError( 

"'c' argument must either be valid as mpl color(s) " 

"or as numbers to be mapped to colors. " 

"Here c = {}." # <- beware, could be long depending on c. 

.format(c) 

) 

else: 

colors = None # use cmap, norm after collection is created 

 

# `delete_masked_points` only modifies arguments of the same length as 

# `x`. 

x, y, s, c, colors, edgecolors, linewidths =\ 

cbook.delete_masked_points( 

x, y, s, c, colors, edgecolors, linewidths) 

 

scales = s # Renamed for readability below. 

 

# to be API compatible 

if verts is not None: 

cbook.warn_deprecated("3.0", name="'verts'", obj_type="kwarg", 

alternative="'marker'") 

if marker is None: 

marker = verts 

 

# load default marker from rcParams 

if marker is None: 

marker = rcParams['scatter.marker'] 

 

if isinstance(marker, mmarkers.MarkerStyle): 

marker_obj = marker 

else: 

marker_obj = mmarkers.MarkerStyle(marker) 

 

path = marker_obj.get_path().transformed( 

marker_obj.get_transform()) 

if not marker_obj.is_filled(): 

edgecolors = 'face' 

linewidths = rcParams['lines.linewidth'] 

 

offsets = np.column_stack([x, y]) 

 

collection = mcoll.PathCollection( 

(path,), scales, 

facecolors=colors, 

edgecolors=edgecolors, 

linewidths=linewidths, 

offsets=offsets, 

transOffset=kwargs.pop('transform', self.transData), 

alpha=alpha 

) 

collection.set_transform(mtransforms.IdentityTransform()) 

collection.update(kwargs) 

 

if colors is None: 

if norm is not None and not isinstance(norm, mcolors.Normalize): 

raise ValueError( 

"'norm' must be an instance of 'mcolors.Normalize'") 

collection.set_array(np.asarray(c)) 

collection.set_cmap(cmap) 

collection.set_norm(norm) 

 

if vmin is not None or vmax is not None: 

collection.set_clim(vmin, vmax) 

else: 

collection.autoscale_None() 

 

# Classic mode only: 

# ensure there are margins to allow for the 

# finite size of the symbols. In v2.x, margins 

# are present by default, so we disable this 

# scatter-specific override. 

if rcParams['_internal.classic_mode']: 

if self._xmargin < 0.05 and x.size > 0: 

self.set_xmargin(0.05) 

if self._ymargin < 0.05 and x.size > 0: 

self.set_ymargin(0.05) 

 

self.add_collection(collection) 

self.autoscale_view() 

 

return collection 

 

@_preprocess_data(replace_names=["x", "y"], label_namer="y") 

@docstring.dedent_interpd 

def hexbin(self, 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, 

**kwargs): 

""" 

Make a hexagonal binning plot. 

 

Make a hexagonal binning plot of *x* versus *y*, where *x*, 

*y* are 1-D sequences of the same length, *N*. If *C* is *None* 

(the default), this is a histogram of the number of occurrences 

of the observations at (x[i],y[i]). 

 

If *C* is specified, it specifies values at the coordinate 

(x[i], y[i]). These values are accumulated for each hexagonal 

bin and then reduced according to *reduce_C_function*, which 

defaults to `numpy.mean`. (If *C* is specified, it must also 

be a 1-D sequence of the same length as *x* and *y*.) 

 

Parameters 

---------- 

x, y : array or masked array 

 

C : array or masked array, optional, default is *None* 

 

gridsize : int or (int, int), optional, default is 100 

The number of hexagons in the *x*-direction, default is 

100. The corresponding number of hexagons in the 

*y*-direction is chosen such that the hexagons are 

approximately regular. Alternatively, gridsize can be a 

tuple with two elements specifying the number of hexagons 

in the *x*-direction and the *y*-direction. 

 

bins : 'log' or int or sequence, optional, default is *None* 

If *None*, no binning is applied; the color of each hexagon 

directly corresponds to its count value. 

 

If 'log', use a logarithmic scale for the color 

map. Internally, :math:`log_{10}(i+1)` is used to 

determine the hexagon color. 

 

If an integer, divide the counts in the specified number 

of bins, and color the hexagons accordingly. 

 

If a sequence of values, the values of the lower bound of 

the bins to be used. 

 

xscale : {'linear', 'log'}, optional, default is 'linear' 

Use a linear or log10 scale on the horizontal axis. 

 

yscale : {'linear', 'log'}, optional, default is 'linear' 

Use a linear or log10 scale on the vertical axis. 

 

mincnt : int > 0, optional, default is *None* 

If not *None*, only display cells with more than *mincnt* 

number of points in the cell 

 

marginals : bool, optional, default is *False* 

if marginals is *True*, plot the marginal density as 

colormapped rectagles along the bottom of the x-axis and 

left of the y-axis 

 

extent : scalar, optional, default is *None* 

The limits of the bins. The default assigns the limits 

based on *gridsize*, *x*, *y*, *xscale* and *yscale*. 

 

If *xscale* or *yscale* is set to 'log', the limits are 

expected to be the exponent for a power of 10. E.g. for 

x-limits of 1 and 50 in 'linear' scale and y-limits 

of 10 and 1000 in 'log' scale, enter (1, 50, 1, 3). 

 

Order of scalars is (left, right, bottom, top). 

 

Other Parameters 

---------------- 

cmap : object, optional, default is *None* 

a :class:`matplotlib.colors.Colormap` instance. If *None*, 

defaults to rc ``image.cmap``. 

 

norm : object, optional, default is *None* 

:class:`matplotlib.colors.Normalize` instance is used to 

scale luminance data to 0,1. 

 

vmin, vmax : scalar, optional, default is *None* 

*vmin* and *vmax* are used in conjunction with *norm* to 

normalize luminance data. If *None*, the min and max of the 

color array *C* are used. Note if you pass a norm instance 

your settings for *vmin* and *vmax* will be ignored. 

 

alpha : scalar between 0 and 1, optional, default is *None* 

the alpha value for the patches 

 

linewidths : scalar, optional, default is *None* 

If *None*, defaults to 1.0. 

 

edgecolors : {'face', 'none', *None*} or color, optional 

 

If 'face' (the default), draws the edges in the same color as the 

fill color. 

 

If 'none', no edge is drawn; this can sometimes lead to unsightly 

unpainted pixels between the hexagons. 

 

If *None*, draws outlines in the default color. 

 

If a matplotlib color arg, draws outlines in the specified color. 

 

Returns 

------- 

polycollection 

A `.PolyCollection` instance; use `.PolyCollection.get_array` on 

this to get the counts in each hexagon. 

 

If *marginals* is *True*, horizontal 

bar and vertical bar (both PolyCollections) will be attached 

to the return collection as attributes *hbar* and *vbar*. 

 

Notes 

----- 

The standard descriptions of all the 

:class:`~matplotlib.collections.Collection` parameters: 

 

%(Collection)s 

 

""" 

self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs) 

 

x, y, C = cbook.delete_masked_points(x, y, C) 

 

# Set the size of the hexagon grid 

if iterable(gridsize): 

nx, ny = gridsize 

else: 

nx = gridsize 

ny = int(nx / math.sqrt(3)) 

# Count the number of data in each hexagon 

x = np.array(x, float) 

y = np.array(y, float) 

if xscale == 'log': 

if np.any(x <= 0.0): 

raise ValueError("x contains non-positive values, so can not" 

" be log-scaled") 

x = np.log10(x) 

if yscale == 'log': 

if np.any(y <= 0.0): 

raise ValueError("y contains non-positive values, so can not" 

" be log-scaled") 

y = np.log10(y) 

if extent is not None: 

xmin, xmax, ymin, ymax = extent 

else: 

xmin, xmax = (np.min(x), np.max(x)) if len(x) else (0, 1) 

ymin, ymax = (np.min(y), np.max(y)) if len(y) else (0, 1) 

 

# to avoid issues with singular data, expand the min/max pairs 

xmin, xmax = mtransforms.nonsingular(xmin, xmax, expander=0.1) 

ymin, ymax = mtransforms.nonsingular(ymin, ymax, expander=0.1) 

 

# In the x-direction, the hexagons exactly cover the region from 

# xmin to xmax. Need some padding to avoid roundoff errors. 

padding = 1.e-9 * (xmax - xmin) 

xmin -= padding 

xmax += padding 

sx = (xmax - xmin) / nx 

sy = (ymax - ymin) / ny 

 

if marginals: 

xorig = x.copy() 

yorig = y.copy() 

 

x = (x - xmin) / sx 

y = (y - ymin) / sy 

ix1 = np.round(x).astype(int) 

iy1 = np.round(y).astype(int) 

ix2 = np.floor(x).astype(int) 

iy2 = np.floor(y).astype(int) 

 

nx1 = nx + 1 

ny1 = ny + 1 

nx2 = nx 

ny2 = ny 

n = nx1 * ny1 + nx2 * ny2 

 

d1 = (x - ix1) ** 2 + 3.0 * (y - iy1) ** 2 

d2 = (x - ix2 - 0.5) ** 2 + 3.0 * (y - iy2 - 0.5) ** 2 

bdist = (d1 < d2) 

if C is None: 

lattice1 = np.zeros((nx1, ny1)) 

lattice2 = np.zeros((nx2, ny2)) 

 

cond1 = (0 <= ix1) * (ix1 < nx1) * (0 <= iy1) * (iy1 < ny1) 

cond2 = (0 <= ix2) * (ix2 < nx2) * (0 <= iy2) * (iy2 < ny2) 

 

cond1 *= bdist 

cond2 *= np.logical_not(bdist) 

ix1, iy1 = ix1[cond1], iy1[cond1] 

ix2, iy2 = ix2[cond2], iy2[cond2] 

 

for ix, iy in zip(ix1, iy1): 

lattice1[ix, iy] += 1 

for ix, iy in zip(ix2, iy2): 

lattice2[ix, iy] += 1 

 

# threshold 

if mincnt is not None: 

lattice1[lattice1 < mincnt] = np.nan 

lattice2[lattice2 < mincnt] = np.nan 

accum = np.hstack((lattice1.ravel(), 

lattice2.ravel())) 

good_idxs = ~np.isnan(accum) 

 

else: 

if mincnt is None: 

mincnt = 0 

 

# create accumulation arrays 

lattice1 = np.empty((nx1, ny1), dtype=object) 

for i in range(nx1): 

for j in range(ny1): 

lattice1[i, j] = [] 

lattice2 = np.empty((nx2, ny2), dtype=object) 

for i in range(nx2): 

for j in range(ny2): 

lattice2[i, j] = [] 

 

for i in range(len(x)): 

if bdist[i]: 

if 0 <= ix1[i] < nx1 and 0 <= iy1[i] < ny1: 

lattice1[ix1[i], iy1[i]].append(C[i]) 

else: 

if 0 <= ix2[i] < nx2 and 0 <= iy2[i] < ny2: 

lattice2[ix2[i], iy2[i]].append(C[i]) 

 

for i in range(nx1): 

for j in range(ny1): 

vals = lattice1[i, j] 

if len(vals) > mincnt: 

lattice1[i, j] = reduce_C_function(vals) 

else: 

lattice1[i, j] = np.nan 

for i in range(nx2): 

for j in range(ny2): 

vals = lattice2[i, j] 

if len(vals) > mincnt: 

lattice2[i, j] = reduce_C_function(vals) 

else: 

lattice2[i, j] = np.nan 

 

accum = np.hstack((lattice1.astype(float).ravel(), 

lattice2.astype(float).ravel())) 

good_idxs = ~np.isnan(accum) 

 

offsets = np.zeros((n, 2), float) 

offsets[:nx1 * ny1, 0] = np.repeat(np.arange(nx1), ny1) 

offsets[:nx1 * ny1, 1] = np.tile(np.arange(ny1), nx1) 

offsets[nx1 * ny1:, 0] = np.repeat(np.arange(nx2) + 0.5, ny2) 

offsets[nx1 * ny1:, 1] = np.tile(np.arange(ny2), nx2) + 0.5 

offsets[:, 0] *= sx 

offsets[:, 1] *= sy 

offsets[:, 0] += xmin 

offsets[:, 1] += ymin 

# remove accumulation bins with no data 

offsets = offsets[good_idxs, :] 

accum = accum[good_idxs] 

 

polygon = np.zeros((6, 2), float) 

polygon[:, 0] = sx * np.array([0.5, 0.5, 0.0, -0.5, -0.5, 0.0]) 

polygon[:, 1] = sy * np.array([-0.5, 0.5, 1.0, 0.5, -0.5, -1.0]) / 3.0 

 

if linewidths is None: 

linewidths = [1.0] 

 

if xscale == 'log' or yscale == 'log': 

polygons = np.expand_dims(polygon, 0) + np.expand_dims(offsets, 1) 

if xscale == 'log': 

polygons[:, :, 0] = 10.0 ** polygons[:, :, 0] 

xmin = 10.0 ** xmin 

xmax = 10.0 ** xmax 

self.set_xscale(xscale) 

if yscale == 'log': 

polygons[:, :, 1] = 10.0 ** polygons[:, :, 1] 

ymin = 10.0 ** ymin 

ymax = 10.0 ** ymax 

self.set_yscale(yscale) 

collection = mcoll.PolyCollection( 

polygons, 

edgecolors=edgecolors, 

linewidths=linewidths, 

) 

else: 

collection = mcoll.PolyCollection( 

[polygon], 

edgecolors=edgecolors, 

linewidths=linewidths, 

offsets=offsets, 

transOffset=mtransforms.IdentityTransform(), 

offset_position="data" 

) 

 

# Check for valid norm 

if norm is not None and not isinstance(norm, mcolors.Normalize): 

msg = "'norm' must be an instance of 'mcolors.Normalize'" 

raise ValueError(msg) 

 

# Set normalizer if bins is 'log' 

if bins == 'log': 

if norm is not None: 

warnings.warn("Only one of 'bins' and 'norm' arguments can be " 

"supplied, ignoring bins={}".format(bins)) 

else: 

norm = mcolors.LogNorm() 

bins = None 

 

if isinstance(norm, mcolors.LogNorm): 

if (accum == 0).any(): 

# make sure we have no zeros 

accum += 1 

 

# autoscale the norm with curren accum values if it hasn't 

# been set 

if norm is not None: 

if norm.vmin is None and norm.vmax is None: 

norm.autoscale(accum) 

 

if bins is not None: 

if not iterable(bins): 

minimum, maximum = min(accum), max(accum) 

bins -= 1 # one less edge than bins 

bins = minimum + (maximum - minimum) * np.arange(bins) / bins 

bins = np.sort(bins) 

accum = bins.searchsorted(accum) 

 

collection.set_array(accum) 

collection.set_cmap(cmap) 

collection.set_norm(norm) 

collection.set_alpha(alpha) 

collection.update(kwargs) 

 

if vmin is not None or vmax is not None: 

collection.set_clim(vmin, vmax) 

else: 

collection.autoscale_None() 

 

corners = ((xmin, ymin), (xmax, ymax)) 

self.update_datalim(corners) 

collection.sticky_edges.x[:] = [xmin, xmax] 

collection.sticky_edges.y[:] = [ymin, ymax] 

self.autoscale_view(tight=True) 

 

# add the collection last 

self.add_collection(collection, autolim=False) 

if not marginals: 

return collection 

 

if C is None: 

C = np.ones(len(x)) 

 

def coarse_bin(x, y, coarse): 

ind = coarse.searchsorted(x).clip(0, len(coarse) - 1) 

mus = np.zeros(len(coarse)) 

for i in range(len(coarse)): 

yi = y[ind == i] 

if len(yi) > 0: 

mu = reduce_C_function(yi) 

else: 

mu = np.nan 

mus[i] = mu 

return mus 

 

coarse = np.linspace(xmin, xmax, gridsize) 

 

xcoarse = coarse_bin(xorig, C, coarse) 

valid = ~np.isnan(xcoarse) 

verts, values = [], [] 

for i, val in enumerate(xcoarse): 

thismin = coarse[i] 

if i < len(coarse) - 1: 

thismax = coarse[i + 1] 

else: 

thismax = thismin + np.diff(coarse)[-1] 

 

if not valid[i]: 

continue 

 

verts.append([(thismin, 0), 

(thismin, 0.05), 

(thismax, 0.05), 

(thismax, 0)]) 

values.append(val) 

 

values = np.array(values) 

trans = self.get_xaxis_transform(which='grid') 

 

hbar = mcoll.PolyCollection(verts, transform=trans, edgecolors='face') 

 

hbar.set_array(values) 

hbar.set_cmap(cmap) 

hbar.set_norm(norm) 

hbar.set_alpha(alpha) 

hbar.update(kwargs) 

self.add_collection(hbar, autolim=False) 

 

coarse = np.linspace(ymin, ymax, gridsize) 

ycoarse = coarse_bin(yorig, C, coarse) 

valid = ~np.isnan(ycoarse) 

verts, values = [], [] 

for i, val in enumerate(ycoarse): 

thismin = coarse[i] 

if i < len(coarse) - 1: 

thismax = coarse[i + 1] 

else: 

thismax = thismin + np.diff(coarse)[-1] 

if not valid[i]: 

continue 

verts.append([(0, thismin), (0.0, thismax), 

(0.05, thismax), (0.05, thismin)]) 

values.append(val) 

 

values = np.array(values) 

 

trans = self.get_yaxis_transform(which='grid') 

 

vbar = mcoll.PolyCollection(verts, transform=trans, edgecolors='face') 

vbar.set_array(values) 

vbar.set_cmap(cmap) 

vbar.set_norm(norm) 

vbar.set_alpha(alpha) 

vbar.update(kwargs) 

self.add_collection(vbar, autolim=False) 

 

collection.hbar = hbar 

collection.vbar = vbar 

 

def on_changed(collection): 

hbar.set_cmap(collection.get_cmap()) 

hbar.set_clim(collection.get_clim()) 

vbar.set_cmap(collection.get_cmap()) 

vbar.set_clim(collection.get_clim()) 

 

collection.callbacksSM.connect('changed', on_changed) 

 

return collection 

 

@docstring.dedent_interpd 

def arrow(self, x, y, dx, dy, **kwargs): 

""" 

Add an arrow to the axes. 

 

This draws an arrow from ``(x, y)`` to ``(x+dx, y+dy)``. 

 

Parameters 

---------- 

x, y : float 

The x/y-coordinate of the arrow base. 

dx, dy : float 

The length of the arrow along x/y-direction. 

 

Returns 

------- 

arrow : `.FancyArrow` 

The created `.FancyArrow` object. 

 

Other Parameters 

---------------- 

**kwargs 

Optional kwargs (inherited from `.FancyArrow` patch) control the 

arrow construction and properties: 

 

%(FancyArrow)s 

 

Notes 

----- 

The resulting arrow is affected by the axes aspect ratio and limits. 

This may produce an arrow whose head is not square with its stem. To 

create an arrow whose head is square with its stem, 

use :meth:`annotate` for example: 

 

>>> ax.annotate("", xy=(0.5, 0.5), xytext=(0, 0), 

... arrowprops=dict(arrowstyle="->")) 

 

""" 

# Strip away units for the underlying patch since units 

# do not make sense to most patch-like code 

x = self.convert_xunits(x) 

y = self.convert_yunits(y) 

dx = self.convert_xunits(dx) 

dy = self.convert_yunits(dy) 

 

a = mpatches.FancyArrow(x, y, dx, dy, **kwargs) 

self.add_artist(a) 

return a 

 

def quiverkey(self, Q, X, Y, U, label, **kw): 

qk = mquiver.QuiverKey(Q, X, Y, U, label, **kw) 

self.add_artist(qk) 

return qk 

quiverkey.__doc__ = mquiver.QuiverKey.quiverkey_doc 

 

# Handle units for x and y, if they've been passed 

def _quiver_units(self, args, kw): 

if len(args) > 3: 

x, y = args[0:2] 

self._process_unit_info(xdata=x, ydata=y, kwargs=kw) 

x = self.convert_xunits(x) 

y = self.convert_yunits(y) 

return (x, y) + args[2:] 

return args 

 

# args can by a combination if X, Y, U, V, C and all should be replaced 

@_preprocess_data(replace_all_args=True, label_namer=None) 

def quiver(self, *args, **kw): 

# Make sure units are handled for x and y values 

args = self._quiver_units(args, kw) 

 

q = mquiver.Quiver(self, *args, **kw) 

 

self.add_collection(q, autolim=True) 

self.autoscale_view() 

return q 

quiver.__doc__ = mquiver.Quiver.quiver_doc 

 

# args can by either Y or y1,y2,... and all should be replaced 

@_preprocess_data(replace_all_args=True, label_namer=None) 

def stackplot(self, x, *args, **kwargs): 

return mstack.stackplot(self, x, *args, **kwargs) 

stackplot.__doc__ = mstack.stackplot.__doc__ 

 

@_preprocess_data(replace_names=["x", "y", "u", "v", "start_points"], 

label_namer=None) 

def streamplot(self, 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'): 

stream_container = mstream.streamplot( 

self, x, y, u, v, 

density=density, 

linewidth=linewidth, 

color=color, 

cmap=cmap, 

norm=norm, 

arrowsize=arrowsize, 

arrowstyle=arrowstyle, 

minlength=minlength, 

start_points=start_points, 

transform=transform, 

zorder=zorder, 

maxlength=maxlength, 

integration_direction=integration_direction) 

return stream_container 

streamplot.__doc__ = mstream.streamplot.__doc__ 

 

# args can be some combination of X, Y, U, V, C and all should be replaced 

@_preprocess_data(replace_all_args=True, label_namer=None) 

@docstring.dedent_interpd 

def barbs(self, *args, **kw): 

""" 

%(barbs_doc)s 

""" 

# Make sure units are handled for x and y values 

args = self._quiver_units(args, kw) 

 

b = mquiver.Barbs(self, *args, **kw) 

self.add_collection(b, autolim=True) 

self.autoscale_view() 

return b 

 

@_preprocess_data(replace_names=["x", "y"], label_namer=None, 

positional_parameter_names=["x", "y", "c"]) 

def fill(self, *args, **kwargs): 

""" 

Plot filled polygons. 

 

Parameters 

---------- 

args : sequence of x, y, [color] 

Each polygon is defined by the lists of *x* and *y* positions of 

its nodes, optionally followed by a *color* specifier. See 

:mod:`matplotlib.colors` for supported color specifiers. The 

standard color cycle is used for polygons without a color 

specifier. 

 

You can plot multiple polygons by providing multiple *x*, *y*, 

*[color]* groups. 

 

For example, each of the following is legal:: 

 

ax.fill(x, y) # a polygon with default color 

ax.fill(x, y, "b") # a blue polygon 

ax.fill(x, y, x2, y2) # two polygons 

ax.fill(x, y, "b", x2, y2, "r") # a blue and a red polygon 

 

Returns 

------- 

a list of :class:`~matplotlib.patches.Polygon` 

 

Other Parameters 

---------------- 

**kwargs : :class:`~matplotlib.patches.Polygon` properties 

 

Notes 

----- 

Use :meth:`fill_between` if you would like to fill the region between 

two curves. 

""" 

# For compatibility(!), get aliases from Line2D rather than Patch. 

kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D._alias_map) 

 

patches = [] 

for poly in self._get_patches_for_fill(*args, **kwargs): 

self.add_patch(poly) 

patches.append(poly) 

self.autoscale_view() 

return patches 

 

@_preprocess_data(replace_names=["x", "y1", "y2", "where"], 

label_namer=None) 

@docstring.dedent_interpd 

def fill_between(self, x, y1, y2=0, where=None, interpolate=False, 

step=None, **kwargs): 

""" 

Fill the area between two horizontal curves. 

 

The curves are defined by the points (*x*, *y1*) and (*x*, *y2*). This 

creates one or multiple polygons describing the filled area. 

 

You may exclude some horizontal sections from filling using *where*. 

 

By default, the edges connect the given points directly. Use *step* if 

the filling should be a step function, i.e. constant in between *x*. 

 

 

Parameters 

---------- 

x : array (length N) 

The x coordinates of the nodes defining the curves. 

 

y1 : array (length N) or scalar 

The y coordinates of the nodes defining the first curve. 

 

y2 : array (length N) or scalar, optional, default: 0 

The y coordinates of the nodes defining the second curve. 

 

where : array of bool (length N), optional, default: None 

Define *where* to exclude some horizontal regions from being 

filled. The filled regions are defined by the coordinates 

``x[where]``. More precisely, fill between ``x[i]`` and ``x[i+1]`` 

if ``where[i] and where[i+1]``. Note that this definition implies 

that an isolated *True* value between two *False* values in 

*where* will not result in filling. Both sides of the *True* 

position remain unfilled due to the adjacent *False* values. 

 

interpolate : bool, optional 

This option is only relvant if *where* is used and the two curves 

are crossing each other. 

 

Semantically, *where* is often used for *y1* > *y2* or similar. 

By default, the nodes of the polygon defining the filled region 

will only be placed at the positions in the *x* array. Such a 

polygon cannot describe the above semantics close to the 

intersection. The x-sections containing the intersection are 

simply clipped. 

 

Setting *interpolate* to *True* will calculate the actual 

intersection point and extend the filled region up to this point. 

 

step : {'pre', 'post', 'mid'}, optional 

Define *step* if the filling should be a step function, 

i.e. constant in between *x*. The value determines where the 

step will occur: 

 

- 'pre': The y value is continued constantly to the left from 

every *x* position, i.e. the interval ``(x[i-1], x[i]]`` has the 

value ``y[i]``. 

- 'post': The y value is continued constantly to the right from 

every *x* position, i.e. the interval ``[x[i], x[i+1])`` has the 

value ``y[i]``. 

- 'mid': Steps occur half-way between the *x* positions. 

 

Other Parameters 

---------------- 

**kwargs 

All other keyword arguments are passed on to `.PolyCollection`. 

They control the `.Polygon` properties: 

 

%(PolyCollection)s 

 

Returns 

------- 

`.PolyCollection` 

A `.PolyCollection` containing the plotted polygons. 

 

See Also 

-------- 

fill_betweenx : Fill between two sets of x-values. 

 

Notes 

----- 

.. [notes section required to get data note injection right] 

 

""" 

if not rcParams['_internal.classic_mode']: 

kwargs = cbook.normalize_kwargs( 

kwargs, mcoll.Collection._alias_map) 

if not any(c in kwargs for c in ('color', 'facecolor')): 

kwargs['facecolor'] = \ 

self._get_patches_for_fill.get_next_color() 

 

# Handle united data, such as dates 

self._process_unit_info(xdata=x, ydata=y1, kwargs=kwargs) 

self._process_unit_info(ydata=y2) 

 

# Convert the arrays so we can work with them 

x = ma.masked_invalid(self.convert_xunits(x)) 

y1 = ma.masked_invalid(self.convert_yunits(y1)) 

y2 = ma.masked_invalid(self.convert_yunits(y2)) 

 

for name, array in [('x', x), ('y1', y1), ('y2', y2)]: 

if array.ndim > 1: 

raise ValueError('Input passed into argument "%r"' % name + 

'is not 1-dimensional.') 

 

if where is None: 

where = True 

where = where & ~functools.reduce(np.logical_or, 

map(np.ma.getmask, [x, y1, y2])) 

 

x, y1, y2 = np.broadcast_arrays(np.atleast_1d(x), y1, y2) 

 

polys = [] 

for ind0, ind1 in cbook.contiguous_regions(where): 

xslice = x[ind0:ind1] 

y1slice = y1[ind0:ind1] 

y2slice = y2[ind0:ind1] 

if step is not None: 

step_func = STEP_LOOKUP_MAP["steps-" + step] 

xslice, y1slice, y2slice = step_func(xslice, y1slice, y2slice) 

 

if not len(xslice): 

continue 

 

N = len(xslice) 

X = np.zeros((2 * N + 2, 2), float) 

 

if interpolate: 

def get_interp_point(ind): 

im1 = max(ind - 1, 0) 

x_values = x[im1:ind + 1] 

diff_values = y1[im1:ind + 1] - y2[im1:ind + 1] 

y1_values = y1[im1:ind + 1] 

 

if len(diff_values) == 2: 

if np.ma.is_masked(diff_values[1]): 

return x[im1], y1[im1] 

elif np.ma.is_masked(diff_values[0]): 

return x[ind], y1[ind] 

 

diff_order = diff_values.argsort() 

diff_root_x = np.interp( 

0, diff_values[diff_order], x_values[diff_order]) 

x_order = x_values.argsort() 

diff_root_y = np.interp(diff_root_x, x_values[x_order], 

y1_values[x_order]) 

return diff_root_x, diff_root_y 

 

start = get_interp_point(ind0) 

end = get_interp_point(ind1) 

else: 

# the purpose of the next two lines is for when y2 is a 

# scalar like 0 and we want the fill to go all the way 

# down to 0 even if none of the y1 sample points do 

start = xslice[0], y2slice[0] 

end = xslice[-1], y2slice[-1] 

 

X[0] = start 

X[N + 1] = end 

 

X[1:N + 1, 0] = xslice 

X[1:N + 1, 1] = y1slice 

X[N + 2:, 0] = xslice[::-1] 

X[N + 2:, 1] = y2slice[::-1] 

 

polys.append(X) 

 

collection = mcoll.PolyCollection(polys, **kwargs) 

 

# now update the datalim and autoscale 

XY1 = np.array([x[where], y1[where]]).T 

XY2 = np.array([x[where], y2[where]]).T 

self.dataLim.update_from_data_xy(XY1, self.ignore_existing_data_limits, 

updatex=True, updatey=True) 

self.ignore_existing_data_limits = False 

self.dataLim.update_from_data_xy(XY2, self.ignore_existing_data_limits, 

updatex=False, updatey=True) 

self.add_collection(collection, autolim=False) 

self.autoscale_view() 

return collection 

 

@_preprocess_data(replace_names=["y", "x1", "x2", "where"], 

label_namer=None) 

@docstring.dedent_interpd 

def fill_betweenx(self, y, x1, x2=0, where=None, 

step=None, interpolate=False, **kwargs): 

""" 

Fill the area between two vertical curves. 

 

The curves are defined by the points (*x1*, *y*) and (*x2*, *y*). This 

creates one or multiple polygons describing the filled area. 

 

You may exclude some vertical sections from filling using *where*. 

 

By default, the edges connect the given points directly. Use *step* if 

the filling should be a step function, i.e. constant in between *y*. 

 

 

Parameters 

---------- 

y : array (length N) 

The y coordinates of the nodes defining the curves. 

 

x1 : array (length N) or scalar 

The x coordinates of the nodes defining the first curve. 

 

x2 : array (length N) or scalar, optional, default: 0 

The x coordinates of the nodes defining the second curve. 

 

where : array of bool (length N), optional, default: None 

Define *where* to exclude some vertical regions from being 

filled. The filled regions are defined by the coordinates 

``y[where]``. More precisely, fill between ``y[i]`` and ``y[i+1]`` 

if ``where[i] and where[i+1]``. Note that this definition implies 

that an isolated *True* value between two *False* values in 

*where* will not result in filling. Both sides of the *True* 

position remain unfilled due to the adjacent *False* values. 

 

interpolate : bool, optional 

This option is only relvant if *where* is used and the two curves 

are crossing each other. 

 

Semantically, *where* is often used for *x1* > *x2* or similar. 

By default, the nodes of the polygon defining the filled region 

will only be placed at the positions in the *y* array. Such a 

polygon cannot describe the above semantics close to the 

intersection. The y-sections containing the intersecion are 

simply clipped. 

 

Setting *interpolate* to *True* will calculate the actual 

interscection point and extend the filled region up to this point. 

 

step : {'pre', 'post', 'mid'}, optional 

Define *step* if the filling should be a step function, 

i.e. constant in between *y*. The value determines where the 

step will occur: 

 

- 'pre': The y value is continued constantly to the left from 

every *x* position, i.e. the interval ``(x[i-1], x[i]]`` has the 

value ``y[i]``. 

- 'post': The y value is continued constantly to the right from 

every *x* position, i.e. the interval ``[x[i], x[i+1])`` has the 

value ``y[i]``. 

- 'mid': Steps occur half-way between the *x* positions. 

 

Other Parameters 

---------------- 

**kwargs 

All other keyword arguments are passed on to `.PolyCollection`. 

They control the `.Polygon` properties: 

 

%(PolyCollection)s 

 

Returns 

------- 

`.PolyCollection` 

A `.PolyCollection` containing the plotted polygons. 

 

See Also 

-------- 

fill_between : Fill between two sets of y-values. 

 

Notes 

----- 

.. [notes section required to get data note injection right] 

 

""" 

if not rcParams['_internal.classic_mode']: 

kwargs = cbook.normalize_kwargs( 

kwargs, mcoll.Collection._alias_map) 

if not any(c in kwargs for c in ('color', 'facecolor')): 

kwargs['facecolor'] = \ 

self._get_patches_for_fill.get_next_color() 

 

# Handle united data, such as dates 

self._process_unit_info(ydata=y, xdata=x1, kwargs=kwargs) 

self._process_unit_info(xdata=x2) 

 

# Convert the arrays so we can work with them 

y = ma.masked_invalid(self.convert_yunits(y)) 

x1 = ma.masked_invalid(self.convert_xunits(x1)) 

x2 = ma.masked_invalid(self.convert_xunits(x2)) 

 

for name, array in [('y', y), ('x1', x1), ('x2', x2)]: 

if array.ndim > 1: 

raise ValueError('Input passed into argument "%r"' % name + 

'is not 1-dimensional.') 

 

if where is None: 

where = True 

where = where & ~functools.reduce(np.logical_or, 

map(np.ma.getmask, [y, x1, x2])) 

 

y, x1, x2 = np.broadcast_arrays(np.atleast_1d(y), x1, x2) 

 

polys = [] 

for ind0, ind1 in cbook.contiguous_regions(where): 

yslice = y[ind0:ind1] 

x1slice = x1[ind0:ind1] 

x2slice = x2[ind0:ind1] 

if step is not None: 

step_func = STEP_LOOKUP_MAP["steps-" + step] 

yslice, x1slice, x2slice = step_func(yslice, x1slice, x2slice) 

 

if not len(yslice): 

continue 

 

N = len(yslice) 

Y = np.zeros((2 * N + 2, 2), float) 

if interpolate: 

def get_interp_point(ind): 

im1 = max(ind - 1, 0) 

y_values = y[im1:ind + 1] 

diff_values = x1[im1:ind + 1] - x2[im1:ind + 1] 

x1_values = x1[im1:ind + 1] 

 

if len(diff_values) == 2: 

if np.ma.is_masked(diff_values[1]): 

return x1[im1], y[im1] 

elif np.ma.is_masked(diff_values[0]): 

return x1[ind], y[ind] 

 

diff_order = diff_values.argsort() 

diff_root_y = np.interp( 

0, diff_values[diff_order], y_values[diff_order]) 

y_order = y_values.argsort() 

diff_root_x = np.interp(diff_root_y, y_values[y_order], 

x1_values[y_order]) 

return diff_root_x, diff_root_y 

 

start = get_interp_point(ind0) 

end = get_interp_point(ind1) 

else: 

# the purpose of the next two lines is for when x2 is a 

# scalar like 0 and we want the fill to go all the way 

# down to 0 even if none of the x1 sample points do 

start = x2slice[0], yslice[0] 

end = x2slice[-1], yslice[-1] 

 

Y[0] = start 

Y[N + 1] = end 

 

Y[1:N + 1, 0] = x1slice 

Y[1:N + 1, 1] = yslice 

Y[N + 2:, 0] = x2slice[::-1] 

Y[N + 2:, 1] = yslice[::-1] 

 

polys.append(Y) 

 

collection = mcoll.PolyCollection(polys, **kwargs) 

 

# now update the datalim and autoscale 

X1Y = np.array([x1[where], y[where]]).T 

X2Y = np.array([x2[where], y[where]]).T 

self.dataLim.update_from_data_xy(X1Y, self.ignore_existing_data_limits, 

updatex=True, updatey=True) 

self.ignore_existing_data_limits = False 

self.dataLim.update_from_data_xy(X2Y, self.ignore_existing_data_limits, 

updatex=True, updatey=False) 

self.add_collection(collection, autolim=False) 

self.autoscale_view() 

return collection 

 

#### plotting z(x,y): imshow, pcolor and relatives, contour 

@_preprocess_data(label_namer=None) 

def imshow(self, 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, **kwargs): 

""" 

Display an image, i.e. data on a 2D regular raster. 

 

Parameters 

---------- 

X : array-like or PIL image 

The image data. Supported array shapes are: 

 

- (M, N): an image with scalar data. The data is visualized 

using a colormap. 

- (M, N, 3): an image with RGB values (float or uint8). 

- (M, N, 4): an image with RGBA values (float or uint8), i.e. 

including transparency. 

 

The first two dimensions (M, N) define the rows and columns of 

the image. 

 

The RGB(A) values should be in the range [0 .. 1] for floats or 

[0 .. 255] for integers. Out-of-range values will be clipped to 

these bounds. 

 

cmap : str or `~matplotlib.colors.Colormap`, optional 

A Colormap instance or registered colormap name. The colormap 

maps scalar data to colors. It is ignored for RGB(A) data. 

Defaults to :rc:`image.cmap`. 

 

aspect : {'equal', 'auto'} or float, optional 

Controls the aspect ratio of the axes. The aspect is of particular 

relevance for images since it may distort the image, i.e. pixel 

will not be square. 

 

This parameter is a shortcut for explicitly calling 

`.Axes.set_aspect`. See there for further details. 

 

- 'equal': Ensures an aspect ratio of 1. Pixels will be square 

(unless pixel sizes are explicitly made non-square in data 

coordinates using *extent*). 

- 'auto': The axes is kept fixed and the aspect is adjusted so 

that the data fit in the axes. In general, this will result in 

non-square pixels. 

 

If not given, use :rc:`image.aspect` (default: 'equal'). 

 

interpolation : str, optional 

The interpolation method used. If *None* 

:rc:`image.interpolation` is used, which defaults to 'nearest'. 

 

Supported values are 'none', 'nearest', 'bilinear', 'bicubic', 

'spline16', 'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 

'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 

'lanczos'. 

 

If *interpolation* is 'none', then no interpolation is performed 

on the Agg, ps and pdf backends. Other backends will fall back to 

'nearest'. 

 

See 

:doc:`/gallery/images_contours_and_fields/interpolation_methods` 

for an overview of the supported interpolation methods. 

 

Some interpolation methods require an additional radius parameter, 

which can be set by *filterrad*. Additionally, the antigrain image 

resize filter is controlled by the parameter *filternorm*. 

 

norm : `~matplotlib.colors.Normalize`, optional 

If scalar data are used, the Normalize instance scales the 

data values to the canonical colormap range [0,1] for mapping 

to colors. By default, the data range is mapped to the 

colorbar range using linear scaling. This parameter is ignored for 

RGB(A) data. 

 

vmin, vmax : scalar, optional 

When using scalar data and no explicit *norm*, *vmin* and *vmax* 

define the data range that the colormap covers. By default, 

the colormap covers the complete value range of the supplied 

data. *vmin*, *vmax* are ignored if the *norm* parameter is used. 

 

alpha : scalar, optional 

The alpha blending value, between 0 (transparent) and 1 (opaque). 

This parameter is ignored for RGBA input data. 

 

origin : {'upper', 'lower'}, optional 

Place the [0,0] index of the array in the upper left or lower left 

corner of the axes. The convention 'upper' is typically used for 

matrices and images. 

If not given, :rc:`image.origin` is used, defaulting to 'upper'. 

 

Note that the vertical axes points upward for 'lower' 

but downward for 'upper'. 

 

extent : scalars (left, right, bottom, top), optional 

The bounding box in data coordinates that the image will fill. 

The image is stretched individually along x and y to fill the box. 

 

The default extent is determined by the following conditions. 

Pixels have unit size in data coordinates. Their centers are on 

integer coordinates, and their center coordinates range from 0 to 

columns-1 horizontally and from 0 to rows-1 vertically. 

 

Note that the direction of the vertical axis and thus the default 

values for top and bottom depend on *origin*: 

 

- For ``origin == 'upper'`` the default is 

``(-0.5, numcols-0.5, numrows-0.5, -0.5)``. 

- For ``origin == 'lower'`` the default is 

``(-0.5, numcols-0.5, -0.5, numrows-0.5)``. 

 

See the example :doc:`/tutorials/intermediate/imshow_extent` for a 

more detailed description. 

 

shape : scalars (columns, rows), optional, default: None 

For raw buffer images. 

 

filternorm : bool, optional, default: True 

A parameter for the antigrain image resize filter (see the 

antigrain documentation). If *filternorm* is set, the filter 

normalizes integer values and corrects the rounding errors. It 

doesn't do anything with the source floating point values, it 

corrects only integers according to the rule of 1.0 which means 

that any sum of pixel weights must be equal to 1.0. So, the 

filter function must produce a graph of the proper shape. 

 

filterrad : float > 0, optional, default: 4.0 

The filter radius for filters that have a radius parameter, i.e. 

when interpolation is one of: 'sinc', 'lanczos' or 'blackman'. 

 

resample : bool, optional 

When *True*, use a full resampling method. When *False*, only 

resample when the output image is larger than the input image. 

 

url : str, optional 

Set the url of the created `.AxesImage`. See `.Artist.set_url`. 

 

Returns 

------- 

image : `~matplotlib.image.AxesImage` 

 

Other Parameters 

---------------- 

**kwargs : `~matplotlib.artist.Artist` properties 

These parameters are passed on to the constructor of the 

`.AxesImage` artist. 

 

See also 

-------- 

matshow : Plot a matrix or an array as an image. 

 

Notes 

----- 

Unless *extent* is used, pixel centers will be located at integer 

coordinates. In other words: the origin will coincide with the center 

of pixel (0, 0). 

 

There are two common representations for RGB images with an alpha 

channel: 

 

- Straight (unassociated) alpha: R, G, and B channels represent the 

color of the pixel, disregarding its opacity. 

- Premultiplied (associated) alpha: R, G, and B channels represent 

the color of the pixel, adjusted for its opacity by multiplication. 

 

`~matplotlib.pyplot.imshow` expects RGB images adopting the straight 

(unassociated) alpha representation. 

""" 

if norm is not None and not isinstance(norm, mcolors.Normalize): 

raise ValueError( 

"'norm' must be an instance of 'mcolors.Normalize'") 

if aspect is None: 

aspect = rcParams['image.aspect'] 

self.set_aspect(aspect) 

im = mimage.AxesImage(self, cmap, norm, interpolation, origin, extent, 

filternorm=filternorm, filterrad=filterrad, 

resample=resample, **kwargs) 

 

im.set_data(X) 

im.set_alpha(alpha) 

if im.get_clip_path() is None: 

# image does not already have clipping set, clip to axes patch 

im.set_clip_path(self.patch) 

if vmin is not None or vmax is not None: 

im.set_clim(vmin, vmax) 

else: 

im.autoscale_None() 

im.set_url(url) 

 

# update ax.dataLim, and, if autoscaling, set viewLim 

# to tightly fit the image, regardless of dataLim. 

im.set_extent(im.get_extent()) 

 

self.add_image(im) 

return im 

 

@staticmethod 

def _pcolorargs(funcname, *args, allmatch=False): 

# If allmatch is True, then the incoming X, Y, C must have matching 

# dimensions, taking into account that X and Y can be 1-D rather than 

# 2-D. This perfect match is required for Gouroud shading. For flat 

# shading, X and Y specify boundaries, so we need one more boundary 

# than color in each direction. For convenience, and consistent with 

# Matlab, we discard the last row and/or column of C if necessary to 

# meet this condition. This is done if allmatch is False. 

 

if len(args) == 1: 

C = np.asanyarray(args[0]) 

numRows, numCols = C.shape 

if allmatch: 

X, Y = np.meshgrid(np.arange(numCols), np.arange(numRows)) 

else: 

X, Y = np.meshgrid(np.arange(numCols + 1), 

np.arange(numRows + 1)) 

C = cbook.safe_masked_invalid(C) 

return X, Y, C 

 

if len(args) == 3: 

# Check x and y for bad data... 

C = np.asanyarray(args[2]) 

X, Y = [cbook.safe_masked_invalid(a) for a in args[:2]] 

if funcname == 'pcolormesh': 

if np.ma.is_masked(X) or np.ma.is_masked(Y): 

raise ValueError( 

'x and y arguments to pcolormesh cannot have ' 

'non-finite values or be of type ' 

'numpy.ma.core.MaskedArray with masked values') 

# safe_masked_invalid() returns an ndarray for dtypes other 

# than floating point. 

if isinstance(X, np.ma.core.MaskedArray): 

X = X.data # strip mask as downstream doesn't like it... 

if isinstance(Y, np.ma.core.MaskedArray): 

Y = Y.data 

numRows, numCols = C.shape 

else: 

raise TypeError( 

'Illegal arguments to %s; see help(%s)' % (funcname, funcname)) 

 

Nx = X.shape[-1] 

Ny = Y.shape[0] 

if X.ndim != 2 or X.shape[0] == 1: 

x = X.reshape(1, Nx) 

X = x.repeat(Ny, axis=0) 

if Y.ndim != 2 or Y.shape[1] == 1: 

y = Y.reshape(Ny, 1) 

Y = y.repeat(Nx, axis=1) 

if X.shape != Y.shape: 

raise TypeError( 

'Incompatible X, Y inputs to %s; see help(%s)' % ( 

funcname, funcname)) 

if allmatch: 

if (Nx, Ny) != (numCols, numRows): 

raise TypeError('Dimensions of C %s are incompatible with' 

' X (%d) and/or Y (%d); see help(%s)' % ( 

C.shape, Nx, Ny, funcname)) 

else: 

if not (numCols in (Nx, Nx - 1) and numRows in (Ny, Ny - 1)): 

raise TypeError('Dimensions of C %s are incompatible with' 

' X (%d) and/or Y (%d); see help(%s)' % ( 

C.shape, Nx, Ny, funcname)) 

C = C[:Ny - 1, :Nx - 1] 

C = cbook.safe_masked_invalid(C) 

return X, Y, C 

 

@_preprocess_data(label_namer=None) 

@docstring.dedent_interpd 

def pcolor(self, *args, alpha=None, norm=None, cmap=None, vmin=None, 

vmax=None, **kwargs): 

r""" 

Create a pseudocolor plot with a non-regular rectangular grid. 

 

Call signature:: 

 

pcolor([X, Y,] C, **kwargs) 

 

*X* and *Y* can be used to specify the corners of the quadrilaterals. 

 

.. hint:: 

 

``pcolor()`` can be very slow for large arrays. In most 

cases you should use the similar but much faster 

`~.Axes.pcolormesh` instead. See there for a discussion of the 

differences. 

 

Parameters 

---------- 

C : array_like 

A scalar 2-D array. The values will be color-mapped. 

 

X, Y : array_like, optional 

The coordinates of the quadrilateral corners. The quadrilateral 

for ``C[i,j]`` has corners at:: 

 

(X[i+1, j], Y[i+1, j]) (X[i+1, j+1], Y[i+1, j+1]) 

+--------+ 

| C[i,j] | 

+--------+ 

(X[i, j], Y[i, j]) (X[i, j+1], Y[i, j+1]), 

 

Note that the column index corresponds to the 

x-coordinate, and the row index corresponds to y. For 

details, see the :ref:`Notes <axes-pcolor-grid-orientation>` 

section below. 

 

The dimensions of *X* and *Y* should be one greater than those of 

*C*. Alternatively, *X*, *Y* and *C* may have equal dimensions, in 

which case the last row and column of *C* will be ignored. 

 

If *X* and/or *Y* are 1-D arrays or column vectors they will be 

expanded as needed into the appropriate 2-D arrays, making a 

rectangular grid. 

 

cmap : str or `~matplotlib.colors.Colormap`, optional 

A Colormap instance or registered colormap name. The colormap 

maps the *C* values to colors. Defaults to :rc:`image.cmap`. 

 

norm : `~matplotlib.colors.Normalize`, optional 

The Normalize instance scales the data values to the canonical 

colormap range [0, 1] for mapping to colors. By default, the data 

range is mapped to the colorbar range using linear scaling. 

 

vmin, vmax : scalar, optional, default: None 

The colorbar range. If *None*, suitable min/max values are 

automatically chosen by the `~.Normalize` instance (defaults to 

the respective min/max values of *C* in case of the default linear 

scaling). 

 

edgecolors : {'none', None, 'face', color, color sequence}, optional 

The color of the edges. Defaults to 'none'. Possible values: 

 

- 'none' or '': No edge. 

- *None*: :rc:`patch.edgecolor` will be used. Note that currently 

:rc:`patch.force_edgecolor` has to be True for this to work. 

- 'face': Use the adjacent face color. 

- An mpl color or sequence of colors will set the edge color. 

 

The singular form *edgecolor* works as an alias. 

 

alpha : scalar, optional, default: None 

The alpha blending value of the face color, between 0 (transparent) 

and 1 (opaque). Note: The edgecolor is currently not affected by 

this. 

 

snap : bool, optional, default: False 

Whether to snap the mesh to pixel boundaries. 

 

Returns 

------- 

collection : `matplotlib.collections.Collection` 

 

Other Parameters 

---------------- 

antialiaseds : bool, optional, default: False 

The default *antialiaseds* is False if the default 

*edgecolors*\ ="none" is used. This eliminates artificial lines 

at patch boundaries, and works regardless of the value of alpha. 

If *edgecolors* is not "none", then the default *antialiaseds* 

is taken from :rc:`patch.antialiased`, which defaults to True. 

Stroking the edges may be preferred if *alpha* is 1, but will 

cause artifacts otherwise. 

 

**kwargs : 

Additionally, the following arguments are allowed. They are passed 

along to the `~matplotlib.collections.PolyCollection` constructor: 

 

%(PolyCollection)s 

 

See Also 

-------- 

pcolormesh : for an explanation of the differences between 

pcolor and pcolormesh. 

imshow : If *X* and *Y* are each equidistant, `~.Axes.imshow` can be a 

faster alternative. 

 

Notes 

----- 

 

**Masked arrays** 

 

*X*, *Y* and *C* may be masked arrays. If either ``C[i, j]``, or one 

of the vertices surrounding ``C[i,j]`` (*X* or *Y* at 

``[i, j], [i+1, j], [i, j+1], [i+1, j+1]``) is masked, nothing is 

plotted. 

 

.. _axes-pcolor-grid-orientation: 

 

**Grid orientation** 

 

The grid orientation follows the standard matrix convention: An array 

*C* with shape (nrows, ncolumns) is plotted with the column number as 

*X* and the row number as *Y*. 

 

**Handling of pcolor() end-cases** 

 

``pcolor()`` displays all columns of *C* if *X* and *Y* are not 

specified, or if *X* and *Y* have one more column than *C*. 

If *X* and *Y* have the same number of columns as *C* then the last 

column of *C* is dropped. Similarly for the rows. 

 

Note: This behavior is different from MATLAB's ``pcolor()``, which 

always discards the last row and column of *C*. 

""" 

X, Y, C = self._pcolorargs('pcolor', *args, allmatch=False) 

Ny, Nx = X.shape 

 

# unit conversion allows e.g. datetime objects as axis values 

self._process_unit_info(xdata=X, ydata=Y, kwargs=kwargs) 

X = self.convert_xunits(X) 

Y = self.convert_yunits(Y) 

 

# convert to MA, if necessary. 

C = ma.asarray(C) 

X = ma.asarray(X) 

Y = ma.asarray(Y) 

 

mask = ma.getmaskarray(X) + ma.getmaskarray(Y) 

xymask = (mask[0:-1, 0:-1] + mask[1:, 1:] + 

mask[0:-1, 1:] + mask[1:, 0:-1]) 

# don't plot if C or any of the surrounding vertices are masked. 

mask = ma.getmaskarray(C) + xymask 

 

compress = np.compress 

 

ravelmask = (mask == 0).ravel() 

X1 = compress(ravelmask, ma.filled(X[:-1, :-1]).ravel()) 

Y1 = compress(ravelmask, ma.filled(Y[:-1, :-1]).ravel()) 

X2 = compress(ravelmask, ma.filled(X[1:, :-1]).ravel()) 

Y2 = compress(ravelmask, ma.filled(Y[1:, :-1]).ravel()) 

X3 = compress(ravelmask, ma.filled(X[1:, 1:]).ravel()) 

Y3 = compress(ravelmask, ma.filled(Y[1:, 1:]).ravel()) 

X4 = compress(ravelmask, ma.filled(X[:-1, 1:]).ravel()) 

Y4 = compress(ravelmask, ma.filled(Y[:-1, 1:]).ravel()) 

npoly = len(X1) 

 

xy = np.stack([X1, Y1, X2, Y2, X3, Y3, X4, Y4, X1, Y1], axis=-1) 

verts = xy.reshape((npoly, 5, 2)) 

 

C = compress(ravelmask, ma.filled(C[0:Ny - 1, 0:Nx - 1]).ravel()) 

 

linewidths = (0.25,) 

if 'linewidth' in kwargs: 

kwargs['linewidths'] = kwargs.pop('linewidth') 

kwargs.setdefault('linewidths', linewidths) 

 

if 'edgecolor' in kwargs: 

kwargs['edgecolors'] = kwargs.pop('edgecolor') 

ec = kwargs.setdefault('edgecolors', 'none') 

 

# aa setting will default via collections to patch.antialiased 

# unless the boundary is not stroked, in which case the 

# default will be False; with unstroked boundaries, aa 

# makes artifacts that are often disturbing. 

if 'antialiased' in kwargs: 

kwargs['antialiaseds'] = kwargs.pop('antialiased') 

if 'antialiaseds' not in kwargs and cbook._str_lower_equal(ec, "none"): 

kwargs['antialiaseds'] = False 

 

kwargs.setdefault('snap', False) 

 

collection = mcoll.PolyCollection(verts, **kwargs) 

 

collection.set_alpha(alpha) 

collection.set_array(C) 

if norm is not None and not isinstance(norm, mcolors.Normalize): 

raise ValueError( 

"'norm' must be an instance of 'mcolors.Normalize'") 

collection.set_cmap(cmap) 

collection.set_norm(norm) 

collection.set_clim(vmin, vmax) 

collection.autoscale_None() 

self.grid(False) 

 

x = X.compressed() 

y = Y.compressed() 

 

# Transform from native to data coordinates? 

t = collection._transform 

if (not isinstance(t, mtransforms.Transform) and 

hasattr(t, '_as_mpl_transform')): 

t = t._as_mpl_transform(self.axes) 

 

if t and any(t.contains_branch_seperately(self.transData)): 

trans_to_data = t - self.transData 

pts = np.vstack([x, y]).T.astype(float) 

transformed_pts = trans_to_data.transform(pts) 

x = transformed_pts[..., 0] 

y = transformed_pts[..., 1] 

 

self.add_collection(collection, autolim=False) 

 

minx = np.min(x) 

maxx = np.max(x) 

miny = np.min(y) 

maxy = np.max(y) 

collection.sticky_edges.x[:] = [minx, maxx] 

collection.sticky_edges.y[:] = [miny, maxy] 

corners = (minx, miny), (maxx, maxy) 

self.update_datalim(corners) 

self.autoscale_view() 

return collection 

 

@_preprocess_data(label_namer=None) 

@docstring.dedent_interpd 

def pcolormesh(self, *args, alpha=None, norm=None, cmap=None, vmin=None, 

vmax=None, shading='flat', antialiased=False, **kwargs): 

""" 

Create a pseudocolor plot with a non-regular rectangular grid. 

 

Call signature:: 

 

pcolor([X, Y,] C, **kwargs) 

 

*X* and *Y* can be used to specify the corners of the quadrilaterals. 

 

.. note:: 

 

``pcolormesh()`` is similar to :func:`~Axes.pcolor`. It's much 

faster and preferred in most cases. For a detailed discussion on 

the differences see 

:ref:`Differences between pcolor() and pcolormesh() 

<differences-pcolor-pcolormesh>`. 

 

Parameters 

---------- 

C : array_like 

A scalar 2-D array. The values will be color-mapped. 

 

X, Y : array_like, optional 

The coordinates of the quadrilateral corners. The quadrilateral 

for ``C[i,j]`` has corners at:: 

 

(X[i+1, j], Y[i+1, j]) (X[i+1, j+1], Y[i+1, j+1]) 

+--------+ 

| C[i,j] | 

+--------+ 

(X[i, j], Y[i, j]) (X[i, j+1], Y[i, j+1]), 

 

Note that the column index corresponds to the 

x-coordinate, and the row index corresponds to y. For 

details, see the :ref:`Notes <axes-pcolormesh-grid-orientation>` 

section below. 

 

The dimensions of *X* and *Y* should be one greater than those of 

*C*. Alternatively, *X*, *Y* and *C* may have equal dimensions, in 

which case the last row and column of *C* will be ignored. 

 

If *X* and/or *Y* are 1-D arrays or column vectors they will be 

expanded as needed into the appropriate 2-D arrays, making a 

rectangular grid. 

 

cmap : str or `~matplotlib.colors.Colormap`, optional 

A Colormap instance or registered colormap name. The colormap 

maps the *C* values to colors. Defaults to :rc:`image.cmap`. 

 

norm : `~matplotlib.colors.Normalize`, optional 

The Normalize instance scales the data values to the canonical 

colormap range [0, 1] for mapping to colors. By default, the data 

range is mapped to the colorbar range using linear scaling. 

 

vmin, vmax : scalar, optional, default: None 

The colorbar range. If *None*, suitable min/max values are 

automatically chosen by the `~.Normalize` instance (defaults to 

the respective min/max values of *C* in case of the default linear 

scaling). 

 

edgecolors : {'none', None, 'face', color, color sequence}, optional 

The color of the edges. Defaults to 'none'. Possible values: 

 

- 'none' or '': No edge. 

- *None*: :rc:`patch.edgecolor` will be used. Note that currently 

:rc:`patch.force_edgecolor` has to be True for this to work. 

- 'face': Use the adjacent face color. 

- An mpl color or sequence of colors will set the edge color. 

 

The singular form *edgecolor* works as an alias. 

 

alpha : scalar, optional, default: None 

The alpha blending value, between 0 (transparent) and 1 (opaque). 

 

shading : {'flat', 'gouraud'}, optional 

The fill style, Possible values: 

 

- 'flat': A solid color is used for each quad. The color of the 

quad (i, j), (i+1, j), (i, j+1), (i+1, j+1) is given by 

``C[i,j]``. 

- 'gouraud': Each quad will be Gouraud shaded: The color of the 

corners (i', j') are given by ``C[i',j']``. The color values of 

the area in between is interpolated from the corner values. 

When Gouraud shading is used, *edgecolors* is ignored. 

 

snap : bool, optional, default: False 

Whether to snap the mesh to pixel boundaries. 

 

Returns 

------- 

mesh : `matplotlib.collections.QuadMesh` 

 

Other Parameters 

---------------- 

**kwargs 

Additionally, the following arguments are allowed. They are passed 

along to the `~matplotlib.collections.QuadMesh` constructor: 

 

%(QuadMesh)s 

 

 

See Also 

-------- 

pcolor : An alternative implementation with slightly different 

features. For a detailed discussion on the differences see 

:ref:`Differences between pcolor() and pcolormesh() 

<differences-pcolor-pcolormesh>`. 

imshow : If *X* and *Y* are each equidistant, `~.Axes.imshow` can be a 

faster alternative. 

 

Notes 

----- 

 

**Masked arrays** 

 

*C* may be a masked array. If ``C[i, j]`` is masked, the corresponding 

quadrilateral will be transparent. Masking of *X* and *Y* is not 

supported. Use `~.Axes.pcolor` if you need this functionality. 

 

.. _axes-pcolormesh-grid-orientation: 

 

**Grid orientation** 

 

The grid orientation follows the standard matrix convention: An array 

*C* with shape (nrows, ncolumns) is plotted with the column number as 

*X* and the row number as *Y*. 

 

.. _differences-pcolor-pcolormesh: 

 

**Differences between pcolor() and pcolormesh()** 

 

Both methods are used to create a pseudocolor plot of a 2-D array 

using quadrilaterals. 

 

The main difference lies in the created object and internal data 

handling: 

While `~.Axes.pcolor` returns a `.PolyCollection`, `~.Axes.pcolormesh` 

returns a `.QuadMesh`. The latter is more specialized for the given 

purpose and thus is faster. It should almost always be preferred. 

 

There is also a slight difference in the handling of masked arrays. 

Both `~.Axes.pcolor` and `~.Axes.pcolormesh` support masked arrays 

for *C*. However, only `~.Axes.pcolor` supports masked arrays for *X* 

and *Y*. The reason lies in the internal handling of the masked values. 

`~.Axes.pcolor` leaves out the respective polygons from the 

PolyCollection. `~.Axes.pcolormesh` sets the facecolor of the masked 

elements to transparent. You can see the difference when using 

edgecolors. While all edges are drawn irrespective of masking in a 

QuadMesh, the edge between two adjacent masked quadrilaterals in 

`~.Axes.pcolor` is not drawn as the corresponding polygons do not 

exist in the PolyCollection. 

 

Another difference is the support of Gouraud shading in 

`~.Axes.pcolormesh`, which is not available with `~.Axes.pcolor`. 

 

""" 

shading = shading.lower() 

kwargs.setdefault('edgecolors', 'None') 

 

allmatch = (shading == 'gouraud') 

 

X, Y, C = self._pcolorargs('pcolormesh', *args, allmatch=allmatch) 

Ny, Nx = X.shape 

X = X.ravel() 

Y = Y.ravel() 

# unit conversion allows e.g. datetime objects as axis values 

self._process_unit_info(xdata=X, ydata=Y, kwargs=kwargs) 

X = self.convert_xunits(X) 

Y = self.convert_yunits(Y) 

 

# convert to one dimensional arrays 

C = C.ravel() 

coords = np.column_stack((X, Y)).astype(float, copy=False) 

collection = mcoll.QuadMesh(Nx - 1, Ny - 1, coords, 

antialiased=antialiased, shading=shading, 

**kwargs) 

collection.set_alpha(alpha) 

collection.set_array(C) 

if norm is not None and not isinstance(norm, mcolors.Normalize): 

raise ValueError( 

"'norm' must be an instance of 'mcolors.Normalize'") 

collection.set_cmap(cmap) 

collection.set_norm(norm) 

collection.set_clim(vmin, vmax) 

collection.autoscale_None() 

 

self.grid(False) 

 

# Transform from native to data coordinates? 

t = collection._transform 

if (not isinstance(t, mtransforms.Transform) and 

hasattr(t, '_as_mpl_transform')): 

t = t._as_mpl_transform(self.axes) 

 

if t and any(t.contains_branch_seperately(self.transData)): 

trans_to_data = t - self.transData 

coords = trans_to_data.transform(coords) 

 

self.add_collection(collection, autolim=False) 

 

minx, miny = np.min(coords, axis=0) 

maxx, maxy = np.max(coords, axis=0) 

collection.sticky_edges.x[:] = [minx, maxx] 

collection.sticky_edges.y[:] = [miny, maxy] 

corners = (minx, miny), (maxx, maxy) 

self.update_datalim(corners) 

self.autoscale_view() 

return collection 

 

@_preprocess_data(label_namer=None) 

@docstring.dedent_interpd 

def pcolorfast(self, *args, alpha=None, norm=None, cmap=None, vmin=None, 

vmax=None, **kwargs): 

""" 

Create a pseudocolor plot with a non-regular rectangular grid. 

 

Call signatures:: 

 

ax.pcolorfast(C, **kwargs) 

ax.pcolorfast(xr, yr, C, **kwargs) 

ax.pcolorfast(x, y, C, **kwargs) 

ax.pcolorfast(X, Y, C, **kwargs) 

 

This method is similar to ~.Axes.pcolor` and `~.Axes.pcolormesh`. 

It's designed to provide the fastest pcolor-type plotting with the 

Agg backend. To achieve this, it uses different algorithms internally 

depending on the complexity of the input grid (regular rectangular, 

non-regular rectangular or arbitrary quadrilateral). 

 

.. warning:: 

 

This method is experimental. Compared to `~.Axes.pcolor` or 

`~.Axes.pcolormesh` it has some limitations: 

 

- It supports only flat shading (no outlines) 

- It lacks support for log scaling of the axes. 

- It does not have a have a pyplot wrapper. 

 

Parameters 

---------- 

C : array-like(M, N) 

A scalar 2D array. The values will be color-mapped. 

*C* may be a masked array. 

 

x, y : tuple or array-like 

*X* and *Y* are used to specify the coordinates of the 

quadilaterals. There are different ways to do this: 

 

- Use tuples ``xr=(xmin, xmax)`` and ``yr=(ymin, ymax)`` to define 

a *uniform rectiangular grid*. 

 

The tuples define the outer edges of the grid. All individual 

quadrilaterals will be of the same size. This is the fastest 

version. 

 

- Use 1D arrays *x*, *y* to specify a *non-uniform rectangular 

grid*. 

 

In this case *x* and *y* have to be monotonic 1D arrays of length 

*N+1* and *M+1*, specifying the x and y boundaries of the cells. 

 

The speed is intermediate. Note: The grid is checked, and if 

found to be uniform the fast version is used. 

 

- Use 2D arrays *X*, *Y* if you need an *arbitrary quadrilateral 

grid* (i.e. if the quadrilaterals are not rectangular). 

 

In this case *X* and *Y* are 2D arrays with shape (M, N), 

specifying the x and y coordinates of the corners of the colored 

quadrilaterals. See `~.Axes.pcolormesh` for details. 

 

This is the most general, but the slowest to render. It may 

produce faster and more compact output using ps, pdf, and 

svg backends, however. 

 

Leaving out *x* and *y* defaults to ``xr=(0, N)``, ``yr=(O, M)``. 

 

cmap : str or `~matplotlib.colors.Colormap`, optional 

A Colormap instance or registered colormap name. The colormap 

maps the *C* values to colors. Defaults to :rc:`image.cmap`. 

 

norm : `~matplotlib.colors.Normalize`, optional 

The Normalize instance scales the data values to the canonical 

colormap range [0, 1] for mapping to colors. By default, the data 

range is mapped to the colorbar range using linear scaling. 

 

vmin, vmax : scalar, optional, default: None 

The colorbar range. If *None*, suitable min/max values are 

automatically chosen by the `~.Normalize` instance (defaults to 

the respective min/max values of *C* in case of the default linear 

scaling). 

 

alpha : scalar, optional, default: None 

The alpha blending value, between 0 (transparent) and 1 (opaque). 

 

snap : bool, optional, default: False 

Whether to snap the mesh to pixel boundaries. 

 

Returns 

------- 

image : `.AxesImage` or `.PcolorImage` or `.QuadMesh` 

The return type depends on the type of grid: 

 

- `.AxesImage` for a regular rectangular grid. 

- `.PcolorImage` for a non-regular rectangular grid. 

- `.QuadMesh` for a non-rectangular grid. 

 

Notes 

----- 

.. [notes section required to get data note injection right] 

 

""" 

if norm is not None and not isinstance(norm, mcolors.Normalize): 

raise ValueError( 

"'norm' must be an instance of 'mcolors.Normalize'") 

 

C = args[-1] 

nr, nc = C.shape 

if len(args) == 1: 

style = "image" 

x = [0, nc] 

y = [0, nr] 

elif len(args) == 3: 

x, y = args[:2] 

x = np.asarray(x) 

y = np.asarray(y) 

if x.ndim == 1 and y.ndim == 1: 

if x.size == 2 and y.size == 2: 

style = "image" 

else: 

dx = np.diff(x) 

dy = np.diff(y) 

if (np.ptp(dx) < 0.01 * np.abs(dx.mean()) and 

np.ptp(dy) < 0.01 * np.abs(dy.mean())): 

style = "image" 

else: 

style = "pcolorimage" 

elif x.ndim == 2 and y.ndim == 2: 

style = "quadmesh" 

else: 

raise TypeError("arguments do not match valid signatures") 

else: 

raise TypeError("need 1 argument or 3 arguments") 

 

if style == "quadmesh": 

 

# convert to one dimensional arrays 

# This should also be moved to the QuadMesh class 

 

# data point in each cell is value at lower left corner 

C = ma.ravel(C) 

X = x.ravel() 

Y = y.ravel() 

Nx = nc + 1 

Ny = nr + 1 

 

# The following needs to be cleaned up; the renderer 

# requires separate contiguous arrays for X and Y, 

# but the QuadMesh class requires the 2D array. 

coords = np.empty(((Nx * Ny), 2), np.float64) 

coords[:, 0] = X 

coords[:, 1] = Y 

 

# The QuadMesh class can also be changed to 

# handle relevant superclass kwargs; the initializer 

# should do much more than it does now. 

collection = mcoll.QuadMesh(nc, nr, coords, 0, edgecolors="None") 

collection.set_alpha(alpha) 

collection.set_array(C) 

collection.set_cmap(cmap) 

collection.set_norm(norm) 

self.add_collection(collection, autolim=False) 

xl, xr, yb, yt = X.min(), X.max(), Y.min(), Y.max() 

ret = collection 

 

else: # It's one of the two image styles. 

xl, xr, yb, yt = x[0], x[-1], y[0], y[-1] 

 

if style == "image": 

im = mimage.AxesImage(self, cmap, norm, 

interpolation='nearest', 

origin='lower', 

extent=(xl, xr, yb, yt), 

**kwargs) 

im.set_data(C) 

im.set_alpha(alpha) 

elif style == "pcolorimage": 

im = mimage.PcolorImage(self, x, y, C, 

cmap=cmap, 

norm=norm, 

alpha=alpha, 

**kwargs) 

im.set_extent((xl, xr, yb, yt)) 

self.add_image(im) 

ret = im 

 

if vmin is not None or vmax is not None: 

ret.set_clim(vmin, vmax) 

else: 

ret.autoscale_None() 

 

ret.sticky_edges.x[:] = [xl, xr] 

ret.sticky_edges.y[:] = [yb, yt] 

self.update_datalim(np.array([[xl, yb], [xr, yt]])) 

self.autoscale_view(tight=True) 

return ret 

 

@_preprocess_data() 

def contour(self, *args, **kwargs): 

kwargs['filled'] = False 

contours = mcontour.QuadContourSet(self, *args, **kwargs) 

self.autoscale_view() 

return contours 

contour.__doc__ = mcontour.QuadContourSet._contour_doc 

 

@_preprocess_data() 

def contourf(self, *args, **kwargs): 

kwargs['filled'] = True 

contours = mcontour.QuadContourSet(self, *args, **kwargs) 

self.autoscale_view() 

return contours 

contourf.__doc__ = mcontour.QuadContourSet._contour_doc 

 

def clabel(self, CS, *args, **kwargs): 

return CS.clabel(*args, **kwargs) 

clabel.__doc__ = mcontour.ContourSet.clabel.__doc__ 

 

@docstring.dedent_interpd 

def table(self, **kwargs): 

""" 

Add a table to the current axes. 

 

Call signature:: 

 

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) 

 

Returns a :class:`matplotlib.table.Table` instance. Either `cellText` 

or `cellColours` must be provided. For finer grained control over 

tables, use the :class:`~matplotlib.table.Table` class and add it to 

the axes with :meth:`~matplotlib.axes.Axes.add_table`. 

 

Thanks to John Gill for providing the class and table. 

 

kwargs control the :class:`~matplotlib.table.Table` 

properties: 

 

%(Table)s 

""" 

return mtable.table(self, **kwargs) 

 

#### Data analysis 

 

@_preprocess_data(replace_names=["x", 'weights'], label_namer="x") 

def hist(self, 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, 

**kwargs): 

""" 

Plot a histogram. 

 

Compute and draw the histogram of *x*. The return value is a 

tuple (*n*, *bins*, *patches*) or ([*n0*, *n1*, ...], *bins*, 

[*patches0*, *patches1*,...]) if the input contains multiple 

data. 

 

Multiple data can be provided via *x* as a list of datasets 

of potentially different length ([*x0*, *x1*, ...]), or as 

a 2-D ndarray in which each column is a dataset. Note that 

the ndarray form is transposed relative to the list form. 

 

Masked arrays are not supported at present. 

 

Parameters 

---------- 

x : (n,) array or sequence of (n,) arrays 

Input values, this takes either a single array or a sequence of 

arrays which are not required to be of the same length. 

 

bins : int or sequence or str, optional 

If an integer is given, ``bins + 1`` bin edges are calculated and 

returned, consistent with `numpy.histogram`. 

 

If `bins` is a sequence, gives bin edges, including left edge of 

first bin and right edge of last bin. In this case, `bins` is 

returned unmodified. 

 

All but the last (righthand-most) bin is half-open. In other 

words, if `bins` is:: 

 

[1, 2, 3, 4] 

 

then the first bin is ``[1, 2)`` (including 1, but excluding 2) and 

the second ``[2, 3)``. The last bin, however, is ``[3, 4]``, which 

*includes* 4. 

 

Unequally spaced bins are supported if *bins* is a sequence. 

 

With Numpy 1.11 or newer, you can alternatively provide a string 

describing a binning strategy, such as 'auto', 'sturges', 'fd', 

'doane', 'scott', 'rice', 'sturges' or 'sqrt', see 

`numpy.histogram`. 

 

The default is taken from :rc:`hist.bins`. 

 

range : tuple or None, optional 

The lower and upper range of the bins. Lower and upper outliers 

are ignored. If not provided, *range* is ``(x.min(), x.max())``. 

Range has no effect if *bins* is a sequence. 

 

If *bins* is a sequence or *range* is specified, autoscaling 

is based on the specified bin range instead of the 

range of x. 

 

Default is ``None`` 

 

density : bool, optional 

If ``True``, the first element of the return tuple will 

be the counts normalized to form a probability density, i.e., 

the area (or integral) under the histogram will sum to 1. 

This is achieved by dividing the count by the number of 

observations times the bin width and not dividing by the total 

number of observations. If *stacked* is also ``True``, the sum of 

the histograms is normalized to 1. 

 

Default is ``None`` for both *normed* and *density*. If either is 

set, then that value will be used. If neither are set, then the 

args will be treated as ``False``. 

 

If both *density* and *normed* are set an error is raised. 

 

weights : (n, ) array_like or None, optional 

An array of weights, of the same shape as *x*. Each value in *x* 

only contributes its associated weight towards the bin count 

(instead of 1). If *normed* or *density* is ``True``, 

the weights are normalized, so that the integral of the density 

over the range remains 1. 

 

Default is ``None`` 

 

cumulative : bool, optional 

If ``True``, then a histogram is computed where each bin gives the 

counts in that bin plus all bins for smaller values. The last bin 

gives the total number of datapoints. If *normed* or *density* 

is also ``True`` then the histogram is normalized such that the 

last bin equals 1. If *cumulative* evaluates to less than 0 

(e.g., -1), the direction of accumulation is reversed. 

In this case, if *normed* and/or *density* is also ``True``, then 

the histogram is normalized such that the first bin equals 1. 

 

Default is ``False`` 

 

bottom : array_like, scalar, or None 

Location of the bottom baseline of each bin. If a scalar, 

the base line for each bin is shifted by the same amount. 

If an array, each bin is shifted independently and the length 

of bottom must match the number of bins. If None, defaults to 0. 

 

Default is ``None`` 

 

histtype : {'bar', 'barstacked', 'step', 'stepfilled'}, optional 

The type of histogram to draw. 

 

- 'bar' is a traditional bar-type histogram. If multiple data 

are given the bars are arranged side by side. 

 

- 'barstacked' is a bar-type histogram where multiple 

data are stacked on top of each other. 

 

- 'step' generates a lineplot that is by default 

unfilled. 

 

- 'stepfilled' generates a lineplot that is by default 

filled. 

 

Default is 'bar' 

 

align : {'left', 'mid', 'right'}, optional 

Controls how the histogram is plotted. 

 

- 'left': bars are centered on the left bin edges. 

 

- 'mid': bars are centered between the bin edges. 

 

- 'right': bars are centered on the right bin edges. 

 

Default is 'mid' 

 

orientation : {'horizontal', 'vertical'}, optional 

If 'horizontal', `~matplotlib.pyplot.barh` will be used for 

bar-type histograms and the *bottom* kwarg will be the left edges. 

 

rwidth : scalar or None, optional 

The relative width of the bars as a fraction of the bin width. If 

``None``, automatically compute the width. 

 

Ignored if *histtype* is 'step' or 'stepfilled'. 

 

Default is ``None`` 

 

log : bool, optional 

If ``True``, the histogram axis will be set to a log scale. If 

*log* is ``True`` and *x* is a 1D array, empty bins will be 

filtered out and only the non-empty ``(n, bins, patches)`` 

will be returned. 

 

Default is ``False`` 

 

color : color or array_like of colors or None, optional 

Color spec or sequence of color specs, one per dataset. Default 

(``None``) uses the standard line color sequence. 

 

Default is ``None`` 

 

label : str or None, optional 

String, or sequence of strings to match multiple datasets. Bar 

charts yield multiple patches per dataset, but only the first gets 

the label, so that the legend command will work as expected. 

 

default is ``None`` 

 

stacked : bool, optional 

If ``True``, multiple data are stacked on top of each other If 

``False`` multiple data are arranged side by side if histtype is 

'bar' or on top of each other if histtype is 'step' 

 

Default is ``False`` 

 

normed : bool, optional 

Deprecated; use the density keyword argument instead. 

 

Returns 

------- 

n : array or list of arrays 

The values of the histogram bins. See *normed* or *density* 

and *weights* for a description of the possible semantics. 

If input *x* is an array, then this is an array of length 

*nbins*. If input is a sequence of arrays 

``[data1, data2,..]``, then this is a list of arrays with 

the values of the histograms for each of the arrays in the 

same order. 

 

bins : array 

The edges of the bins. Length nbins + 1 (nbins left edges and right 

edge of last bin). Always a single array even when multiple data 

sets are passed in. 

 

patches : list or list of lists 

Silent list of individual patches used to create the histogram 

or list of such list if multiple input datasets. 

 

Other Parameters 

---------------- 

**kwargs : `~matplotlib.patches.Patch` properties 

 

See also 

-------- 

hist2d : 2D histograms 

 

Notes 

----- 

.. [Notes section required for data comment. See #10189.] 

 

""" 

# Avoid shadowing the builtin. 

bin_range = range 

from builtins import range 

 

if np.isscalar(x): 

x = [x] 

 

if bins is None: 

bins = rcParams['hist.bins'] 

 

# Validate string inputs here so we don't have to clutter 

# subsequent code. 

if histtype not in ['bar', 'barstacked', 'step', 'stepfilled']: 

raise ValueError("histtype %s is not recognized" % histtype) 

 

if align not in ['left', 'mid', 'right']: 

raise ValueError("align kwarg %s is not recognized" % align) 

 

if orientation not in ['horizontal', 'vertical']: 

raise ValueError( 

"orientation kwarg %s is not recognized" % orientation) 

 

if histtype == 'barstacked' and not stacked: 

stacked = True 

 

if density is not None and normed is not None: 

raise ValueError("kwargs 'density' and 'normed' cannot be used " 

"simultaneously. " 

"Please only use 'density', since 'normed'" 

"is deprecated.") 

if normed is not None: 

cbook.warn_deprecated("2.1", name="'normed'", obj_type="kwarg", 

alternative="'density'", removal="3.1") 

 

# basic input validation 

input_empty = np.size(x) == 0 

# Massage 'x' for processing. 

if input_empty: 

x = [np.array([])] 

else: 

x = cbook._reshape_2D(x, 'x') 

nx = len(x) # number of datasets 

 

# Process unit information 

# Unit conversion is done individually on each dataset 

self._process_unit_info(xdata=x[0], kwargs=kwargs) 

x = [self.convert_xunits(xi) for xi in x] 

 

if bin_range is not None: 

bin_range = self.convert_xunits(bin_range) 

 

# Check whether bins or range are given explicitly. 

binsgiven = (cbook.iterable(bins) or bin_range is not None) 

 

# We need to do to 'weights' what was done to 'x' 

if weights is not None: 

w = cbook._reshape_2D(weights, 'weights') 

else: 

w = [None] * nx 

 

if len(w) != nx: 

raise ValueError('weights should have the same shape as x') 

 

for xi, wi in zip(x, w): 

if wi is not None and len(wi) != len(xi): 

raise ValueError( 

'weights should have the same shape as x') 

 

if color is None: 

color = [self._get_lines.get_next_color() for i in range(nx)] 

else: 

color = mcolors.to_rgba_array(color) 

if len(color) != nx: 

error_message = ( 

"color kwarg must have one color per data set. %d data " 

"sets and %d colors were provided" % (nx, len(color))) 

raise ValueError(error_message) 

 

# If bins are not specified either explicitly or via range, 

# we need to figure out the range required for all datasets, 

# and supply that to np.histogram. 

if not binsgiven and not input_empty: 

xmin = np.inf 

xmax = -np.inf 

for xi in x: 

if len(xi) > 0: 

xmin = min(xmin, np.nanmin(xi)) 

xmax = max(xmax, np.nanmax(xi)) 

bin_range = (xmin, xmax) 

density = bool(density) or bool(normed) 

if density and not stacked: 

hist_kwargs = dict(range=bin_range, density=density) 

else: 

hist_kwargs = dict(range=bin_range) 

 

# List to store all the top coordinates of the histograms 

tops = [] 

mlast = None 

# Loop through datasets 

for i in range(nx): 

# this will automatically overwrite bins, 

# so that each histogram uses the same bins 

m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs) 

m = m.astype(float) # causes problems later if it's an int 

if mlast is None: 

mlast = np.zeros(len(bins)-1, m.dtype) 

if stacked: 

m += mlast 

mlast[:] = m 

tops.append(m) 

 

# If a stacked density plot, normalize so the area of all the stacked 

# histograms together is 1 

if stacked and density: 

db = np.diff(bins) 

for m in tops: 

m[:] = (m / db) / tops[-1].sum() 

if cumulative: 

slc = slice(None) 

if isinstance(cumulative, Number) and cumulative < 0: 

slc = slice(None, None, -1) 

 

if density: 

tops = [(m * np.diff(bins))[slc].cumsum()[slc] for m in tops] 

else: 

tops = [m[slc].cumsum()[slc] for m in tops] 

 

patches = [] 

 

# Save autoscale state for later restoration; turn autoscaling 

# off so we can do it all a single time at the end, instead 

# of having it done by bar or fill and then having to be redone. 

_saved_autoscalex = self.get_autoscalex_on() 

_saved_autoscaley = self.get_autoscaley_on() 

self.set_autoscalex_on(False) 

self.set_autoscaley_on(False) 

 

if histtype.startswith('bar'): 

 

totwidth = np.diff(bins) 

 

if rwidth is not None: 

dr = np.clip(rwidth, 0, 1) 

elif (len(tops) > 1 and 

((not stacked) or rcParams['_internal.classic_mode'])): 

dr = 0.8 

else: 

dr = 1.0 

 

if histtype == 'bar' and not stacked: 

width = dr * totwidth / nx 

dw = width 

boffset = -0.5 * dr * totwidth * (1 - 1 / nx) 

elif histtype == 'barstacked' or stacked: 

width = dr * totwidth 

boffset, dw = 0.0, 0.0 

 

if align == 'mid' or align == 'edge': 

boffset += 0.5 * totwidth 

elif align == 'right': 

boffset += totwidth 

 

if orientation == 'horizontal': 

_barfunc = self.barh 

bottom_kwarg = 'left' 

else: # orientation == 'vertical' 

_barfunc = self.bar 

bottom_kwarg = 'bottom' 

 

for m, c in zip(tops, color): 

if bottom is None: 

bottom = np.zeros(len(m)) 

if stacked: 

height = m - bottom 

else: 

height = m 

patch = _barfunc(bins[:-1]+boffset, height, width, 

align='center', log=log, 

color=c, **{bottom_kwarg: bottom}) 

patches.append(patch) 

if stacked: 

bottom[:] = m 

boffset += dw 

 

elif histtype.startswith('step'): 

# these define the perimeter of the polygon 

x = np.zeros(4 * len(bins) - 3) 

y = np.zeros(4 * len(bins) - 3) 

 

x[0:2*len(bins)-1:2], x[1:2*len(bins)-1:2] = bins, bins[:-1] 

x[2*len(bins)-1:] = x[1:2*len(bins)-1][::-1] 

 

if bottom is None: 

bottom = np.zeros(len(bins) - 1) 

 

y[1:2*len(bins)-1:2], y[2:2*len(bins):2] = bottom, bottom 

y[2*len(bins)-1:] = y[1:2*len(bins)-1][::-1] 

 

if log: 

if orientation == 'horizontal': 

self.set_xscale('log', nonposx='clip') 

logbase = self.xaxis._scale.base 

else: # orientation == 'vertical' 

self.set_yscale('log', nonposy='clip') 

logbase = self.yaxis._scale.base 

 

# Setting a minimum of 0 results in problems for log plots 

if np.min(bottom) > 0: 

minimum = np.min(bottom) 

elif density or weights is not None: 

# For data that is normed to form a probability density, 

# set to minimum data value / logbase 

# (gives 1 full tick-label unit for the lowest filled bin) 

ndata = np.array(tops) 

minimum = (np.min(ndata[ndata > 0])) / logbase 

else: 

# For non-normed (density = False) data, 

# set the min to 1 / log base, 

# again so that there is 1 full tick-label unit 

# for the lowest bin 

minimum = 1.0 / logbase 

 

y[0], y[-1] = minimum, minimum 

else: 

minimum = 0 

 

if align == 'left' or align == 'center': 

x -= 0.5*(bins[1]-bins[0]) 

elif align == 'right': 

x += 0.5*(bins[1]-bins[0]) 

 

# If fill kwarg is set, it will be passed to the patch collection, 

# overriding this 

fill = (histtype == 'stepfilled') 

 

xvals, yvals = [], [] 

for m in tops: 

if stacked: 

# starting point for drawing polygon 

y[0] = y[1] 

# top of the previous polygon becomes the bottom 

y[2*len(bins)-1:] = y[1:2*len(bins)-1][::-1] 

# set the top of this polygon 

y[1:2*len(bins)-1:2], y[2:2*len(bins):2] = (m + bottom, 

m + bottom) 

if log: 

y[y < minimum] = minimum 

if orientation == 'horizontal': 

xvals.append(y.copy()) 

yvals.append(x.copy()) 

else: 

xvals.append(x.copy()) 

yvals.append(y.copy()) 

 

# stepfill is closed, step is not 

split = -1 if fill else 2 * len(bins) 

# add patches in reverse order so that when stacking, 

# items lower in the stack are plotted on top of 

# items higher in the stack 

for x, y, c in reversed(list(zip(xvals, yvals, color))): 

patches.append(self.fill( 

x[:split], y[:split], 

closed=True if fill else None, 

facecolor=c, 

edgecolor=None if fill else c, 

fill=fill if fill else None)) 

for patch_list in patches: 

for patch in patch_list: 

if orientation == 'vertical': 

patch.sticky_edges.y.append(minimum) 

elif orientation == 'horizontal': 

patch.sticky_edges.x.append(minimum) 

 

# we return patches, so put it back in the expected order 

patches.reverse() 

 

self.set_autoscalex_on(_saved_autoscalex) 

self.set_autoscaley_on(_saved_autoscaley) 

self.autoscale_view() 

 

if label is None: 

labels = [None] 

elif isinstance(label, str): 

labels = [label] 

elif not np.iterable(label): 

labels = [str(label)] 

else: 

labels = [str(lab) for lab in label] 

 

for patch, lbl in itertools.zip_longest(patches, labels): 

if patch: 

p = patch[0] 

p.update(kwargs) 

if lbl is not None: 

p.set_label(lbl) 

 

for p in patch[1:]: 

p.update(kwargs) 

p.set_label('_nolegend_') 

 

if nx == 1: 

return tops[0], bins, cbook.silent_list('Patch', patches[0]) 

else: 

return tops, bins, cbook.silent_list('Lists of Patches', patches) 

 

@_preprocess_data(replace_names=["x", "y", "weights"], label_namer=None) 

def hist2d(self, x, y, bins=10, range=None, normed=False, weights=None, 

cmin=None, cmax=None, **kwargs): 

""" 

Make a 2D histogram plot. 

 

Parameters 

---------- 

x, y : array_like, shape (n, ) 

Input values 

 

bins : None or int or [int, int] or array_like or [array, array] 

 

The bin specification: 

 

- If int, the number of bins for the two dimensions 

(nx=ny=bins). 

 

- If ``[int, int]``, the number of bins in each dimension 

(nx, ny = bins). 

 

- If array_like, the bin edges for the two dimensions 

(x_edges=y_edges=bins). 

 

- If ``[array, array]``, the bin edges in each dimension 

(x_edges, y_edges = bins). 

 

The default value is 10. 

 

range : array_like shape(2, 2), optional, default: None 

The leftmost and rightmost edges of the bins along each dimension 

(if not specified explicitly in the bins parameters): ``[[xmin, 

xmax], [ymin, ymax]]``. All values outside of this range will be 

considered outliers and not tallied in the histogram. 

 

normed : bool, optional, default: False 

Normalize histogram. 

 

weights : array_like, shape (n, ), optional, default: None 

An array of values w_i weighing each sample (x_i, y_i). 

 

cmin : scalar, optional, default: None 

All bins that has count less than cmin will not be displayed and 

these count values in the return value count histogram will also 

be set to nan upon return 

 

cmax : scalar, optional, default: None 

All bins that has count more than cmax will not be displayed (set 

to none before passing to imshow) and these count values in the 

return value count histogram will also be set to nan upon return 

 

Returns 

------- 

h : 2D array 

The bi-dimensional histogram of samples x and y. Values in x are 

histogrammed along the first dimension and values in y are 

histogrammed along the second dimension. 

xedges : 1D array 

The bin edges along the x axis. 

yedges : 1D array 

The bin edges along the y axis. 

image : `~.matplotlib.collections.QuadMesh` 

 

Other Parameters 

---------------- 

cmap : Colormap or str, optional 

A `.colors.Colormap` instance. If not set, use rc settings. 

 

norm : Normalize, optional 

A `.colors.Normalize` instance is used to 

scale luminance data to ``[0, 1]``. If not set, defaults to 

`.colors.Normalize()`. 

 

vmin/vmax : None or scalar, optional 

Arguments passed to the `~.colors.Normalize` instance. 

 

alpha : ``0 <= scalar <= 1`` or ``None``, optional 

The alpha blending value. 

 

See also 

-------- 

hist : 1D histogram plotting 

 

Notes 

----- 

- Currently ``hist2d`` calculates it's own axis limits, and any limits 

previously set are ignored. 

- Rendering the histogram with a logarithmic color scale is 

accomplished by passing a `.colors.LogNorm` instance to the *norm* 

keyword argument. Likewise, power-law normalization (similar 

in effect to gamma correction) can be accomplished with 

`.colors.PowerNorm`. 

""" 

 

h, xedges, yedges = np.histogram2d(x, y, bins=bins, range=range, 

normed=normed, weights=weights) 

 

if cmin is not None: 

h[h < cmin] = None 

if cmax is not None: 

h[h > cmax] = None 

 

pc = self.pcolormesh(xedges, yedges, h.T, **kwargs) 

self.set_xlim(xedges[0], xedges[-1]) 

self.set_ylim(yedges[0], yedges[-1]) 

 

return h, xedges, yedges, pc 

 

@_preprocess_data(replace_names=["x"], label_namer=None) 

@docstring.dedent_interpd 

def psd(self, 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, **kwargs): 

r""" 

Plot the power spectral density. 

 

Call signature:: 

 

psd(x, 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, return_line=None, **kwargs) 

 

The power spectral density :math:`P_{xx}` by Welch's average 

periodogram method. The vector *x* is divided into *NFFT* length 

segments. Each segment is detrended by function *detrend* and 

windowed by function *window*. *noverlap* gives the length of 

the overlap between segments. The :math:`|\mathrm{fft}(i)|^2` 

of each segment :math:`i` are averaged to compute :math:`P_{xx}`, 

with a scaling to correct for power loss due to windowing. 

 

If len(*x*) < *NFFT*, it will be zero padded to *NFFT*. 

 

Parameters 

---------- 

x : 1-D array or sequence 

Array or sequence containing the data 

 

%(Spectral)s 

 

%(PSD)s 

 

noverlap : int 

The number of points of overlap between segments. 

The default value is 0 (no overlap). 

 

Fc : int 

The center frequency of *x* (defaults to 0), which offsets 

the x extents of the plot to reflect the frequency range used 

when a signal is acquired and then filtered and downsampled to 

baseband. 

 

return_line : bool 

Whether to include the line object plotted in the returned values. 

Default is False. 

 

Returns 

------- 

Pxx : 1-D array 

The values for the power spectrum `P_{xx}` before scaling 

(real valued). 

 

freqs : 1-D array 

The frequencies corresponding to the elements in *Pxx*. 

 

line : a :class:`~matplotlib.lines.Line2D` instance 

The line created by this function. 

Only returned if *return_line* is True. 

 

Other Parameters 

---------------- 

**kwargs : 

Keyword arguments control the :class:`~matplotlib.lines.Line2D` 

properties: 

 

%(Line2D)s 

 

See Also 

-------- 

:func:`specgram` 

:func:`specgram` differs in the default overlap; in not returning 

the mean of the segment periodograms; in returning the times of the 

segments; and in plotting a colormap instead of a line. 

 

:func:`magnitude_spectrum` 

:func:`magnitude_spectrum` plots the magnitude spectrum. 

 

:func:`csd` 

:func:`csd` plots the spectral density between two signals. 

 

Notes 

----- 

For plotting, the power is plotted as 

:math:`10\log_{10}(P_{xx})` for decibels, though *Pxx* itself 

is returned. 

 

References 

---------- 

Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, 

John Wiley & Sons (1986) 

""" 

if Fc is None: 

Fc = 0 

 

pxx, freqs = mlab.psd(x=x, NFFT=NFFT, Fs=Fs, detrend=detrend, 

window=window, noverlap=noverlap, pad_to=pad_to, 

sides=sides, scale_by_freq=scale_by_freq) 

freqs += Fc 

 

if scale_by_freq in (None, True): 

psd_units = 'dB/Hz' 

else: 

psd_units = 'dB' 

 

line = self.plot(freqs, 10 * np.log10(pxx), **kwargs) 

self.set_xlabel('Frequency') 

self.set_ylabel('Power Spectral Density (%s)' % psd_units) 

self.grid(True) 

vmin, vmax = self.viewLim.intervaly 

intv = vmax - vmin 

logi = int(np.log10(intv)) 

if logi == 0: 

logi = .1 

step = 10 * logi 

ticks = np.arange(math.floor(vmin), math.ceil(vmax) + 1, step) 

self.set_yticks(ticks) 

 

if return_line is None or not return_line: 

return pxx, freqs 

else: 

return pxx, freqs, line 

 

@_preprocess_data(replace_names=["x", "y"], label_namer="y") 

@docstring.dedent_interpd 

def csd(self, 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, **kwargs): 

""" 

Plot the cross-spectral density. 

 

Call signature:: 

 

csd(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, return_line=None, **kwargs) 

 

The cross spectral density :math:`P_{xy}` by Welch's average 

periodogram method. The vectors *x* and *y* are divided into 

*NFFT* length segments. Each segment is detrended by function 

*detrend* and windowed by function *window*. *noverlap* gives 

the length of the overlap between segments. The product of 

the direct FFTs of *x* and *y* are averaged over each segment 

to compute :math:`P_{xy}`, with a scaling to correct for power 

loss due to windowing. 

 

If len(*x*) < *NFFT* or len(*y*) < *NFFT*, they will be zero 

padded to *NFFT*. 

 

Parameters 

---------- 

x, y : 1-D arrays or sequences 

Arrays or sequences containing the data. 

 

%(Spectral)s 

 

%(PSD)s 

 

noverlap : int 

The number of points of overlap between segments. 

The default value is 0 (no overlap). 

 

Fc : int 

The center frequency of *x* (defaults to 0), which offsets 

the x extents of the plot to reflect the frequency range used 

when a signal is acquired and then filtered and downsampled to 

baseband. 

 

return_line : bool 

Whether to include the line object plotted in the returned values. 

Default is False. 

 

Returns 

------- 

Pxy : 1-D array 

The values for the cross spectrum `P_{xy}` before scaling 

(complex valued). 

 

freqs : 1-D array 

The frequencies corresponding to the elements in *Pxy*. 

 

line : a :class:`~matplotlib.lines.Line2D` instance 

The line created by this function. 

Only returned if *return_line* is True. 

 

Other Parameters 

---------------- 

**kwargs : 

Keyword arguments control the :class:`~matplotlib.lines.Line2D` 

properties: 

 

%(Line2D)s 

 

See Also 

-------- 

:func:`psd` 

:func:`psd` is the equivalent to setting y=x. 

 

Notes 

----- 

For plotting, the power is plotted as 

:math:`10\\log_{10}(P_{xy})` for decibels, though `P_{xy}` itself 

is returned. 

 

References 

---------- 

Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, 

John Wiley & Sons (1986) 

""" 

if Fc is None: 

Fc = 0 

 

pxy, freqs = mlab.csd(x=x, y=y, NFFT=NFFT, Fs=Fs, detrend=detrend, 

window=window, noverlap=noverlap, pad_to=pad_to, 

sides=sides, scale_by_freq=scale_by_freq) 

# pxy is complex 

freqs += Fc 

 

line = self.plot(freqs, 10 * np.log10(np.abs(pxy)), **kwargs) 

self.set_xlabel('Frequency') 

self.set_ylabel('Cross Spectrum Magnitude (dB)') 

self.grid(True) 

vmin, vmax = self.viewLim.intervaly 

 

intv = vmax - vmin 

step = 10 * int(np.log10(intv)) 

 

ticks = np.arange(math.floor(vmin), math.ceil(vmax) + 1, step) 

self.set_yticks(ticks) 

 

if return_line is None or not return_line: 

return pxy, freqs 

else: 

return pxy, freqs, line 

 

@_preprocess_data(replace_names=["x"], label_namer=None) 

@docstring.dedent_interpd 

def magnitude_spectrum(self, x, Fs=None, Fc=None, window=None, 

pad_to=None, sides=None, scale=None, 

**kwargs): 

""" 

Plot the magnitude spectrum. 

 

Call signature:: 

 

magnitude_spectrum(x, Fs=2, Fc=0, window=mlab.window_hanning, 

pad_to=None, sides='default', **kwargs) 

 

Compute the magnitude spectrum of *x*. Data is padded to a 

length of *pad_to* and the windowing function *window* is applied to 

the signal. 

 

Parameters 

---------- 

x : 1-D array or sequence 

Array or sequence containing the data. 

 

%(Spectral)s 

 

%(Single_Spectrum)s 

 

scale : {'default', 'linear', 'dB'} 

The scaling of the values in the *spec*. 'linear' is no scaling. 

'dB' returns the values in dB scale, i.e., the dB amplitude 

(20 * log10). 'default' is 'linear'. 

 

Fc : int 

The center frequency of *x* (defaults to 0), which offsets 

the x extents of the plot to reflect the frequency range used 

when a signal is acquired and then filtered and downsampled to 

baseband. 

 

Returns 

------- 

spectrum : 1-D array 

The values for the magnitude spectrum before scaling (real valued). 

 

freqs : 1-D array 

The frequencies corresponding to the elements in *spectrum*. 

 

line : a :class:`~matplotlib.lines.Line2D` instance 

The line created by this function. 

 

Other Parameters 

---------------- 

**kwargs : 

Keyword arguments control the :class:`~matplotlib.lines.Line2D` 

properties: 

 

%(Line2D)s 

 

See Also 

-------- 

:func:`psd` 

:func:`psd` plots the power spectral density.`. 

 

:func:`angle_spectrum` 

:func:`angle_spectrum` plots the angles of the corresponding 

frequencies. 

 

:func:`phase_spectrum` 

:func:`phase_spectrum` plots the phase (unwrapped angle) of the 

corresponding frequencies. 

 

:func:`specgram` 

:func:`specgram` can plot the magnitude spectrum of segments within 

the signal in a colormap. 

 

Notes 

----- 

.. [Notes section required for data comment. See #10189.] 

 

""" 

if Fc is None: 

Fc = 0 

 

if scale is None or scale == 'default': 

scale = 'linear' 

 

spec, freqs = mlab.magnitude_spectrum(x=x, Fs=Fs, window=window, 

pad_to=pad_to, sides=sides) 

freqs += Fc 

 

if scale == 'linear': 

Z = spec 

yunits = 'energy' 

elif scale == 'dB': 

Z = 20. * np.log10(spec) 

yunits = 'dB' 

else: 

raise ValueError('Unknown scale %s', scale) 

 

lines = self.plot(freqs, Z, **kwargs) 

self.set_xlabel('Frequency') 

self.set_ylabel('Magnitude (%s)' % yunits) 

 

return spec, freqs, lines[0] 

 

@_preprocess_data(replace_names=["x"], label_namer=None) 

@docstring.dedent_interpd 

def angle_spectrum(self, x, Fs=None, Fc=None, window=None, 

pad_to=None, sides=None, **kwargs): 

""" 

Plot the angle spectrum. 

 

Call signature:: 

 

angle_spectrum(x, Fs=2, Fc=0, window=mlab.window_hanning, 

pad_to=None, sides='default', **kwargs) 

 

Compute the angle spectrum (wrapped phase spectrum) of *x*. 

Data is padded to a length of *pad_to* and the windowing function 

*window* is applied to the signal. 

 

Parameters 

---------- 

x : 1-D array or sequence 

Array or sequence containing the data. 

 

%(Spectral)s 

 

%(Single_Spectrum)s 

 

Fc : int 

The center frequency of *x* (defaults to 0), which offsets 

the x extents of the plot to reflect the frequency range used 

when a signal is acquired and then filtered and downsampled to 

baseband. 

 

Returns 

------- 

spectrum : 1-D array 

The values for the angle spectrum in radians (real valued). 

 

freqs : 1-D array 

The frequencies corresponding to the elements in *spectrum*. 

 

line : a :class:`~matplotlib.lines.Line2D` instance 

The line created by this function. 

 

Other Parameters 

---------------- 

**kwargs : 

Keyword arguments control the :class:`~matplotlib.lines.Line2D` 

properties: 

 

%(Line2D)s 

 

See Also 

-------- 

:func:`magnitude_spectrum` 

:func:`angle_spectrum` plots the magnitudes of the corresponding 

frequencies. 

 

:func:`phase_spectrum` 

:func:`phase_spectrum` plots the unwrapped version of this 

function. 

 

:func:`specgram` 

:func:`specgram` can plot the angle spectrum of segments within the 

signal in a colormap. 

 

Notes 

----- 

.. [Notes section required for data comment. See #10189.] 

 

""" 

if Fc is None: 

Fc = 0 

 

spec, freqs = mlab.angle_spectrum(x=x, Fs=Fs, window=window, 

pad_to=pad_to, sides=sides) 

freqs += Fc 

 

lines = self.plot(freqs, spec, **kwargs) 

self.set_xlabel('Frequency') 

self.set_ylabel('Angle (radians)') 

 

return spec, freqs, lines[0] 

 

@_preprocess_data(replace_names=["x"], label_namer=None) 

@docstring.dedent_interpd 

def phase_spectrum(self, x, Fs=None, Fc=None, window=None, 

pad_to=None, sides=None, **kwargs): 

""" 

Plot the phase spectrum. 

 

Call signature:: 

 

phase_spectrum(x, Fs=2, Fc=0, window=mlab.window_hanning, 

pad_to=None, sides='default', **kwargs) 

 

Compute the phase spectrum (unwrapped angle spectrum) of *x*. 

Data is padded to a length of *pad_to* and the windowing function 

*window* is applied to the signal. 

 

Parameters 

---------- 

x : 1-D array or sequence 

Array or sequence containing the data 

 

%(Spectral)s 

 

%(Single_Spectrum)s 

 

Fc : int 

The center frequency of *x* (defaults to 0), which offsets 

the x extents of the plot to reflect the frequency range used 

when a signal is acquired and then filtered and downsampled to 

baseband. 

 

Returns 

------- 

spectrum : 1-D array 

The values for the phase spectrum in radians (real valued). 

 

freqs : 1-D array 

The frequencies corresponding to the elements in *spectrum*. 

 

line : a :class:`~matplotlib.lines.Line2D` instance 

The line created by this function. 

 

Other Parameters 

---------------- 

**kwargs : 

Keyword arguments control the :class:`~matplotlib.lines.Line2D` 

properties: 

 

%(Line2D)s 

 

See Also 

-------- 

:func:`magnitude_spectrum` 

:func:`magnitude_spectrum` plots the magnitudes of the 

corresponding frequencies. 

 

:func:`angle_spectrum` 

:func:`angle_spectrum` plots the wrapped version of this function. 

 

:func:`specgram` 

:func:`specgram` can plot the phase spectrum of segments within the 

signal in a colormap. 

 

Notes 

----- 

.. [Notes section required for data comment. See #10189.] 

 

""" 

if Fc is None: 

Fc = 0 

 

spec, freqs = mlab.phase_spectrum(x=x, Fs=Fs, window=window, 

pad_to=pad_to, sides=sides) 

freqs += Fc 

 

lines = self.plot(freqs, spec, **kwargs) 

self.set_xlabel('Frequency') 

self.set_ylabel('Phase (radians)') 

 

return spec, freqs, lines[0] 

 

@_preprocess_data(replace_names=["x", "y"], label_namer=None) 

@docstring.dedent_interpd 

def cohere(self, 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, **kwargs): 

""" 

Plot the coherence between *x* and *y*. 

 

Plot the coherence between *x* and *y*. Coherence is the 

normalized cross spectral density: 

 

.. math:: 

 

C_{xy} = \\frac{|P_{xy}|^2}{P_{xx}P_{yy}} 

 

Parameters 

---------- 

%(Spectral)s 

 

%(PSD)s 

 

noverlap : int 

The number of points of overlap between blocks. The 

default value is 0 (no overlap). 

 

Fc : int 

The center frequency of *x* (defaults to 0), which offsets 

the x extents of the plot to reflect the frequency range used 

when a signal is acquired and then filtered and downsampled to 

baseband. 

 

 

Returns 

------- 

Cxy : 1-D array 

The coherence vector. 

 

freqs : 1-D array 

The frequencies for the elements in *Cxy*. 

 

Other Parameters 

---------------- 

**kwargs : 

Keyword arguments control the :class:`~matplotlib.lines.Line2D` 

properties: 

 

%(Line2D)s 

 

References 

---------- 

Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, 

John Wiley & Sons (1986) 

""" 

cxy, freqs = mlab.cohere(x=x, y=y, NFFT=NFFT, Fs=Fs, detrend=detrend, 

window=window, noverlap=noverlap, 

scale_by_freq=scale_by_freq) 

freqs += Fc 

 

self.plot(freqs, cxy, **kwargs) 

self.set_xlabel('Frequency') 

self.set_ylabel('Coherence') 

self.grid(True) 

 

return cxy, freqs 

 

@_preprocess_data(replace_names=["x"], label_namer=None) 

@docstring.dedent_interpd 

def specgram(self, 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, **kwargs): 

""" 

Plot a spectrogram. 

 

Call signature:: 

 

specgram(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none, 

window=mlab.window_hanning, noverlap=128, 

cmap=None, xextent=None, pad_to=None, sides='default', 

scale_by_freq=None, mode='default', scale='default', 

**kwargs) 

 

Compute and plot a spectrogram of data in *x*. Data are split into 

*NFFT* length segments and the spectrum of each section is 

computed. The windowing function *window* is applied to each 

segment, and the amount of overlap of each segment is 

specified with *noverlap*. The spectrogram is plotted as a colormap 

(using imshow). 

 

Parameters 

---------- 

x : 1-D array or sequence 

Array or sequence containing the data. 

 

%(Spectral)s 

 

%(PSD)s 

 

mode : {'default', 'psd', 'magnitude', 'angle', 'phase'} 

What sort of spectrum to use. Default is 'psd', which takes 

the power spectral density. 'complex' returns the complex-valued 

frequency spectrum. 'magnitude' returns the magnitude spectrum. 

'angle' returns the phase spectrum without unwrapping. 'phase' 

returns the phase spectrum with unwrapping. 

 

noverlap : int 

The number of points of overlap between blocks. The 

default value is 128. 

 

scale : {'default', 'linear', 'dB'} 

The scaling of the values in the *spec*. 'linear' is no scaling. 

'dB' returns the values in dB scale. When *mode* is 'psd', 

this is dB power (10 * log10). Otherwise this is dB amplitude 

(20 * log10). 'default' is 'dB' if *mode* is 'psd' or 

'magnitude' and 'linear' otherwise. This must be 'linear' 

if *mode* is 'angle' or 'phase'. 

 

Fc : int 

The center frequency of *x* (defaults to 0), which offsets 

the x extents of the plot to reflect the frequency range used 

when a signal is acquired and then filtered and downsampled to 

baseband. 

 

cmap : 

A :class:`matplotlib.colors.Colormap` instance; if *None*, use 

default determined by rc 

 

xextent : *None* or (xmin, xmax) 

The image extent along the x-axis. The default sets *xmin* to the 

left border of the first bin (*spectrum* column) and *xmax* to the 

right border of the last bin. Note that for *noverlap>0* the width 

of the bins is smaller than those of the segments. 

 

**kwargs : 

Additional kwargs are passed on to imshow which makes the 

specgram image. 

 

Returns 

------- 

spectrum : 2-D array 

Columns are the periodograms of successive segments. 

 

freqs : 1-D array 

The frequencies corresponding to the rows in *spectrum*. 

 

t : 1-D array 

The times corresponding to midpoints of segments (i.e., the columns 

in *spectrum*). 

 

im : instance of class :class:`~matplotlib.image.AxesImage` 

The image created by imshow containing the spectrogram 

 

See Also 

-------- 

:func:`psd` 

:func:`psd` differs in the default overlap; in returning the mean 

of the segment periodograms; in not returning times; and in 

generating a line plot instead of colormap. 

 

:func:`magnitude_spectrum` 

A single spectrum, similar to having a single segment when *mode* 

is 'magnitude'. Plots a line instead of a colormap. 

 

:func:`angle_spectrum` 

A single spectrum, similar to having a single segment when *mode* 

is 'angle'. Plots a line instead of a colormap. 

 

:func:`phase_spectrum` 

A single spectrum, similar to having a single segment when *mode* 

is 'phase'. Plots a line instead of a colormap. 

 

Notes 

----- 

The parameters *detrend* and *scale_by_freq* do only apply when *mode* 

is set to 'psd'. 

""" 

if NFFT is None: 

NFFT = 256 # same default as in mlab.specgram() 

if Fc is None: 

Fc = 0 # same default as in mlab._spectral_helper() 

if noverlap is None: 

noverlap = 128 # same default as in mlab.specgram() 

 

if mode == 'complex': 

raise ValueError('Cannot plot a complex specgram') 

 

if scale is None or scale == 'default': 

if mode in ['angle', 'phase']: 

scale = 'linear' 

else: 

scale = 'dB' 

elif mode in ['angle', 'phase'] and scale == 'dB': 

raise ValueError('Cannot use dB scale with angle or phase mode') 

 

spec, freqs, t = mlab.specgram(x=x, NFFT=NFFT, Fs=Fs, 

detrend=detrend, window=window, 

noverlap=noverlap, pad_to=pad_to, 

sides=sides, 

scale_by_freq=scale_by_freq, 

mode=mode) 

 

if scale == 'linear': 

Z = spec 

elif scale == 'dB': 

if mode is None or mode == 'default' or mode == 'psd': 

Z = 10. * np.log10(spec) 

else: 

Z = 20. * np.log10(spec) 

else: 

raise ValueError('Unknown scale %s', scale) 

 

Z = np.flipud(Z) 

 

if xextent is None: 

# padding is needed for first and last segment: 

pad_xextent = (NFFT-noverlap) / Fs / 2 

xextent = np.min(t) - pad_xextent, np.max(t) + pad_xextent 

xmin, xmax = xextent 

freqs += Fc 

extent = xmin, xmax, freqs[0], freqs[-1] 

im = self.imshow(Z, cmap, extent=extent, vmin=vmin, vmax=vmax, 

**kwargs) 

self.axis('auto') 

 

return spec, freqs, t, im 

 

@docstring.dedent_interpd 

def spy(self, Z, precision=0, marker=None, markersize=None, 

aspect='equal', origin="upper", **kwargs): 

""" 

Plot the sparsity pattern of a 2D array. 

 

This visualizes the non-zero values of the array. 

 

Two plotting styles are available: image and marker. Both 

are available for full arrays, but only the marker style 

works for `scipy.sparse.spmatrix` instances. 

 

**Image style** 

 

If *marker* and *markersize* are *None*, `~.Axes.imshow` is used. Any 

extra remaining kwargs are passed to this method. 

 

**Marker style** 

 

If *Z* is a `scipy.sparse.spmatrix` or *marker* or *markersize* are 

*None*, a `~matplotlib.lines.Line2D` object will be returned with 

the value of marker determining the marker type, and any 

remaining kwargs passed to `~.Axes.plot`. 

 

Parameters 

---------- 

Z : array-like (M, N) 

The array to be plotted. 

 

precision : float or 'present', optional, default: 0 

If *precision* is 0, any non-zero value will be plotted. Otherwise, 

values of :math:`|Z| > precision` will be plotted. 

 

For :class:`scipy.sparse.spmatrix` instances, you can also 

pass 'present'. In this case any value present in the array 

will be plotted, even if it is identically zero. 

 

origin : {'upper', 'lower'}, optional 

Place the [0,0] index of the array in the upper left or lower left 

corner of the axes. The convention 'upper' is typically used for 

matrices and images. 

If not given, :rc:`image.origin` is used, defaulting to 'upper'. 

 

 

aspect : {'equal', 'auto', None} or float, optional 

Controls the aspect ratio of the axes. The aspect is of particular 

relevance for images since it may distort the image, i.e. pixel 

will not be square. 

 

This parameter is a shortcut for explicitly calling 

`.Axes.set_aspect`. See there for further details. 

 

- 'equal': Ensures an aspect ratio of 1. Pixels will be square. 

- 'auto': The axes is kept fixed and the aspect is adjusted so 

that the data fit in the axes. In general, this will result in 

non-square pixels. 

- *None*: Use :rc:`image.aspect` (default: 'equal'). 

 

Default: 'equal' 

 

Returns 

------- 

ret : `~matplotlib.image.AxesImage` or `.Line2D` 

The return type depends on the plotting style (see above). 

 

Other Parameters 

---------------- 

**kwargs 

The supported additional parameters depend on the plotting style. 

 

For the image style, you can pass the following additional 

parameters of `~.Axes.imshow`: 

 

- *cmap* 

- *alpha* 

- *url* 

- any `.Artist` properties (passed on to the `.AxesImage`) 

 

For the marker style, you can pass any `.Line2D` property except 

for *linestyle*: 

 

%(Line2D)s 

""" 

if marker is None and markersize is None and hasattr(Z, 'tocoo'): 

marker = 's' 

if marker is None and markersize is None: 

Z = np.asarray(Z) 

mask = np.abs(Z) > precision 

 

if 'cmap' not in kwargs: 

kwargs['cmap'] = mcolors.ListedColormap(['w', 'k'], 

name='binary') 

nr, nc = Z.shape 

extent = [-0.5, nc - 0.5, nr - 0.5, -0.5] 

ret = self.imshow(mask, interpolation='nearest', aspect=aspect, 

extent=extent, origin=origin, **kwargs) 

else: 

if hasattr(Z, 'tocoo'): 

c = Z.tocoo() 

if precision == 'present': 

y = c.row 

x = c.col 

else: 

nonzero = np.abs(c.data) > precision 

y = c.row[nonzero] 

x = c.col[nonzero] 

else: 

Z = np.asarray(Z) 

nonzero = np.abs(Z) > precision 

y, x = np.nonzero(nonzero) 

if marker is None: 

marker = 's' 

if markersize is None: 

markersize = 10 

marks = mlines.Line2D(x, y, linestyle='None', 

marker=marker, markersize=markersize, **kwargs) 

self.add_line(marks) 

nr, nc = Z.shape 

self.set_xlim(-0.5, nc - 0.5) 

self.set_ylim(nr - 0.5, -0.5) 

self.set_aspect(aspect) 

ret = marks 

self.title.set_y(1.05) 

self.xaxis.tick_top() 

self.xaxis.set_ticks_position('both') 

self.xaxis.set_major_locator(mticker.MaxNLocator(nbins=9, 

steps=[1, 2, 5, 10], 

integer=True)) 

self.yaxis.set_major_locator(mticker.MaxNLocator(nbins=9, 

steps=[1, 2, 5, 10], 

integer=True)) 

return ret 

 

def matshow(self, Z, **kwargs): 

""" 

Plot the values of a 2D matrix or array as color-coded image. 

 

The matrix will be shown the way it would be printed, with the first 

row at the top. Row and column numbering is zero-based. 

 

Parameters 

---------- 

Z : array-like(M, N) 

The matrix to be displayed. 

 

Returns 

------- 

image : `~matplotlib.image.AxesImage` 

 

Other Parameters 

---------------- 

**kwargs : `~matplotlib.axes.Axes.imshow` arguments 

 

See Also 

-------- 

imshow : More general function to plot data on a 2D regular raster. 

 

Notes 

----- 

This is just a convenience function wrapping `.imshow` to set useful 

defaults for a displaying a matrix. In particular: 

 

- Set ``origin='upper'``. 

- Set ``interpolation='nearest'``. 

- Set ``aspect='equal'``. 

- Ticks are placed to the left and above. 

- Ticks are formatted to show integer indices. 

 

""" 

Z = np.asanyarray(Z) 

nr, nc = Z.shape 

kw = {'origin': 'upper', 

'interpolation': 'nearest', 

'aspect': 'equal', # (already the imshow default) 

**kwargs} 

im = self.imshow(Z, **kw) 

self.title.set_y(1.05) 

self.xaxis.tick_top() 

self.xaxis.set_ticks_position('both') 

self.xaxis.set_major_locator(mticker.MaxNLocator(nbins=9, 

steps=[1, 2, 5, 10], 

integer=True)) 

self.yaxis.set_major_locator(mticker.MaxNLocator(nbins=9, 

steps=[1, 2, 5, 10], 

integer=True)) 

return im 

 

@_preprocess_data(replace_names=["dataset"], label_namer=None) 

def violinplot(self, dataset, positions=None, vert=True, widths=0.5, 

showmeans=False, showextrema=True, showmedians=False, 

points=100, bw_method=None): 

""" 

Make a violin plot. 

 

Make a violin plot for each column of *dataset* or each vector in 

sequence *dataset*. Each filled area extends to represent the 

entire data range, with optional lines at the mean, the median, 

the minimum, and the maximum. 

 

Parameters 

---------- 

dataset : Array or a sequence of vectors. 

The input data. 

 

positions : array-like, default = [1, 2, ..., n] 

Sets the positions of the violins. The ticks and limits are 

automatically set to match the positions. 

 

vert : bool, default = True. 

If true, creates a vertical violin plot. 

Otherwise, creates a horizontal violin plot. 

 

widths : array-like, default = 0.5 

Either a scalar or a vector that sets the maximal width of 

each violin. The default is 0.5, which uses about half of the 

available horizontal space. 

 

showmeans : bool, default = False 

If `True`, will toggle rendering of the means. 

 

showextrema : bool, default = True 

If `True`, will toggle rendering of the extrema. 

 

showmedians : bool, default = False 

If `True`, will toggle rendering of the medians. 

 

points : scalar, default = 100 

Defines the number of points to evaluate each of the 

gaussian kernel density estimations at. 

 

bw_method : str, scalar or callable, optional 

The method used to calculate the estimator bandwidth. This can be 

'scott', 'silverman', a scalar constant or a callable. If a 

scalar, this will be used directly as `kde.factor`. If a 

callable, it should take a `GaussianKDE` instance as its only 

parameter and return a scalar. If None (default), 'scott' is used. 

 

Returns 

------- 

 

result : dict 

A dictionary mapping each component of the violinplot to a 

list of the corresponding collection instances created. The 

dictionary has the following keys: 

 

- ``bodies``: A list of the 

:class:`matplotlib.collections.PolyCollection` instances 

containing the filled area of each violin. 

 

- ``cmeans``: A 

:class:`matplotlib.collections.LineCollection` instance 

created to identify the mean values of each of the 

violin's distribution. 

 

- ``cmins``: A 

:class:`matplotlib.collections.LineCollection` instance 

created to identify the bottom of each violin's 

distribution. 

 

- ``cmaxes``: A 

:class:`matplotlib.collections.LineCollection` instance 

created to identify the top of each violin's 

distribution. 

 

- ``cbars``: A 

:class:`matplotlib.collections.LineCollection` instance 

created to identify the centers of each violin's 

distribution. 

 

- ``cmedians``: A 

:class:`matplotlib.collections.LineCollection` instance 

created to identify the median values of each of the 

violin's distribution. 

 

Notes 

----- 

.. [Notes section required for data comment. See #10189.] 

 

""" 

 

def _kde_method(X, coords): 

# fallback gracefully if the vector contains only one value 

if np.all(X[0] == X): 

return (X[0] == coords).astype(float) 

kde = mlab.GaussianKDE(X, bw_method) 

return kde.evaluate(coords) 

 

vpstats = cbook.violin_stats(dataset, _kde_method, points=points) 

return self.violin(vpstats, positions=positions, vert=vert, 

widths=widths, showmeans=showmeans, 

showextrema=showextrema, showmedians=showmedians) 

 

def violin(self, vpstats, positions=None, vert=True, widths=0.5, 

showmeans=False, showextrema=True, showmedians=False): 

"""Drawing function for violin plots. 

 

Draw a violin plot for each column of `vpstats`. Each filled area 

extends to represent the entire data range, with optional lines at the 

mean, the median, the minimum, and the maximum. 

 

Parameters 

---------- 

 

vpstats : list of dicts 

A list of dictionaries containing stats for each violin plot. 

Required keys are: 

 

- ``coords``: A list of scalars containing the coordinates that 

the violin's kernel density estimate were evaluated at. 

 

- ``vals``: A list of scalars containing the values of the 

kernel density estimate at each of the coordinates given 

in *coords*. 

 

- ``mean``: The mean value for this violin's dataset. 

 

- ``median``: The median value for this violin's dataset. 

 

- ``min``: The minimum value for this violin's dataset. 

 

- ``max``: The maximum value for this violin's dataset. 

 

positions : array-like, default = [1, 2, ..., n] 

Sets the positions of the violins. The ticks and limits are 

automatically set to match the positions. 

 

vert : bool, default = True. 

If true, plots the violins veritcally. 

Otherwise, plots the violins horizontally. 

 

widths : array-like, default = 0.5 

Either a scalar or a vector that sets the maximal width of 

each violin. The default is 0.5, which uses about half of the 

available horizontal space. 

 

showmeans : bool, default = False 

If true, will toggle rendering of the means. 

 

showextrema : bool, default = True 

If true, will toggle rendering of the extrema. 

 

showmedians : bool, default = False 

If true, will toggle rendering of the medians. 

 

Returns 

------- 

result : dict 

A dictionary mapping each component of the violinplot to a 

list of the corresponding collection instances created. The 

dictionary has the following keys: 

 

- ``bodies``: A list of the 

:class:`matplotlib.collections.PolyCollection` instances 

containing the filled area of each violin. 

 

- ``cmeans``: A 

:class:`matplotlib.collections.LineCollection` instance 

created to identify the mean values of each of the 

violin's distribution. 

 

- ``cmins``: A 

:class:`matplotlib.collections.LineCollection` instance 

created to identify the bottom of each violin's 

distribution. 

 

- ``cmaxes``: A 

:class:`matplotlib.collections.LineCollection` instance 

created to identify the top of each violin's 

distribution. 

 

- ``cbars``: A 

:class:`matplotlib.collections.LineCollection` instance 

created to identify the centers of each violin's 

distribution. 

 

- ``cmedians``: A 

:class:`matplotlib.collections.LineCollection` instance 

created to identify the median values of each of the 

violin's distribution. 

 

""" 

 

# Statistical quantities to be plotted on the violins 

means = [] 

mins = [] 

maxes = [] 

medians = [] 

 

# Collections to be returned 

artists = {} 

 

N = len(vpstats) 

datashape_message = ("List of violinplot statistics and `{0}` " 

"values must have the same length") 

 

# Validate positions 

if positions is None: 

positions = range(1, N + 1) 

elif len(positions) != N: 

raise ValueError(datashape_message.format("positions")) 

 

# Validate widths 

if np.isscalar(widths): 

widths = [widths] * N 

elif len(widths) != N: 

raise ValueError(datashape_message.format("widths")) 

 

# Calculate ranges for statistics lines 

pmins = -0.25 * np.array(widths) + positions 

pmaxes = 0.25 * np.array(widths) + positions 

 

# Check whether we are rendering vertically or horizontally 

if vert: 

fill = self.fill_betweenx 

perp_lines = self.hlines 

par_lines = self.vlines 

else: 

fill = self.fill_between 

perp_lines = self.vlines 

par_lines = self.hlines 

 

if rcParams['_internal.classic_mode']: 

fillcolor = 'y' 

edgecolor = 'r' 

else: 

fillcolor = edgecolor = self._get_lines.get_next_color() 

 

# Render violins 

bodies = [] 

for stats, pos, width in zip(vpstats, positions, widths): 

# The 0.5 factor reflects the fact that we plot from v-p to 

# v+p 

vals = np.array(stats['vals']) 

vals = 0.5 * width * vals / vals.max() 

bodies += [fill(stats['coords'], 

-vals + pos, 

vals + pos, 

facecolor=fillcolor, 

alpha=0.3)] 

means.append(stats['mean']) 

mins.append(stats['min']) 

maxes.append(stats['max']) 

medians.append(stats['median']) 

artists['bodies'] = bodies 

 

# Render means 

if showmeans: 

artists['cmeans'] = perp_lines(means, pmins, pmaxes, 

colors=edgecolor) 

 

# Render extrema 

if showextrema: 

artists['cmaxes'] = perp_lines(maxes, pmins, pmaxes, 

colors=edgecolor) 

artists['cmins'] = perp_lines(mins, pmins, pmaxes, 

colors=edgecolor) 

artists['cbars'] = par_lines(positions, mins, maxes, 

colors=edgecolor) 

 

# Render medians 

if showmedians: 

artists['cmedians'] = perp_lines(medians, 

pmins, 

pmaxes, 

colors=edgecolor) 

 

return artists 

 

def tricontour(self, *args, **kwargs): 

return mtri.tricontour(self, *args, **kwargs) 

tricontour.__doc__ = mtri.tricontour.__doc__ 

 

def tricontourf(self, *args, **kwargs): 

return mtri.tricontourf(self, *args, **kwargs) 

tricontourf.__doc__ = mtri.tricontour.__doc__ 

 

def tripcolor(self, *args, **kwargs): 

return mtri.tripcolor(self, *args, **kwargs) 

tripcolor.__doc__ = mtri.tripcolor.__doc__ 

 

def triplot(self, *args, **kwargs): 

return mtri.triplot(self, *args, **kwargs) 

triplot.__doc__ = mtri.triplot.__doc__