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hub/venv/lib/python3.7/site-packages/matplotlib/axes/_axes.py

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Add virtual environment to the git repository, this isn't 100% right but it's practical at this development point
2020-06-16 10:34:17 -04:00
import collections.abc
import functools
import itertools
import logging
import math
from numbers import Number
import numpy as np
from numpy import ma
import matplotlib.category as _ # <-registers a category unit converter
import matplotlib.cbook as cbook
import matplotlib.collections as mcoll
import matplotlib.colors as mcolors
import matplotlib.contour as mcontour
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.patches as mpatches
import matplotlib.path as mpath
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 import _preprocess_data, rcParams
from matplotlib.axes._base import _AxesBase, _process_plot_format
from matplotlib.axes._secondary_axes import SecondaryAxis
from matplotlib.container import BarContainer, ErrorbarContainer, StemContainer
try:
from numpy.lib.histograms import (
histogram_bin_edges as _histogram_bin_edges)
except ImportError:
# this function is new in np 1.15
def _histogram_bin_edges(arr, bins, range=None, weights=None):
# this in True for 1D arrays, and False for None and str
if np.ndim(bins) == 1:
return bins
if isinstance(bins, str):
# rather than backporting the internals, just do the full
# computation. If this is too slow for users, they can
# update numpy, or pick a manual number of bins
return np.histogram(arr, bins, range, weights)[1]
else:
if bins is None:
# hard-code numpy's default
bins = 10
if range is None:
range = np.min(arr), np.max(arr)
return np.linspace(*range, bins + 1)
_log = logging.getLogger(__name__)
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 `Axes` contains most of the figure elements: `~.axis.Axis`,
`~.axis.Tick`, `~.lines.Line2D`, `~.text.Text`, `~.patches.Polygon`, etc.,
and sets the coordinate system.
The `Axes` instance supports callbacks through a callbacks attribute which
is a `~.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 `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
@cbook.deprecated("3.1")
@property
def aname(self):
return '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.
"""
titles = {'left': self._left_title,
'center': self.title,
'right': self._right_title}
title = cbook._check_getitem(titles, loc=loc.lower())
return title.get_text()
def set_title(self, label, fontdict=None, loc=None, 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'],
'color' : rcParams['axes.titlecolor'],
'verticalalignment': 'baseline',
'horizontalalignment': loc}
loc : {'center', 'left', 'right'}, str, optional
Which title to set.
If *None*, defaults to :rc:`axes.titlelocation`.
pad : float
The offset of the title from the top of the axes, in points.
If *None*, defaults to :rc:`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.
"""
if loc is None:
loc = rcParams['axes.titlelocation']
titles = {'left': self._left_title,
'center': self.title,
'right': self._right_title}
title = cbook._check_getitem(titles, loc=loc.lower())
default = {
'fontsize': rcParams['axes.titlesize'],
'fontweight': rcParams['axes.titleweight'],
'verticalalignment': 'baseline',
'horizontalalignment': loc.lower()}
titlecolor = rcParams['axes.titlecolor']
if not cbook._str_lower_equal(titlecolor, 'auto'):
default["color"] = titlecolor
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 from the axes bounding box including ticks
and tick labels.
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 from the axes bounding box including ticks
and tick labels.
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, = ax.plot([1, 2, 3])
line.set_label('Label via method')
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 : list of str, 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
-------
legend : `~matplotlib.legend.Legend`
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 keyword arguments are passed on to the `.Axes` child axes.
Returns
-------
ax
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.subplots()
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 : float
Transparency of the rectangle and connector lines. Default is 0.5.
zorder : float
Drawing order of the rectangle and connector lines. Default is 4.99
(just below the default level of inset axes).
**kwargs
Other keyword arguments are passed on to the rectangle patch.
Returns
-------
rectangle_patch : `.patches.Rectangle`
The indicator frame.
connector_lines : 4-tuple of `.patches.ConnectionPatch`
The four connector lines connecting to (lower_left, upper_left,
lower_right upper_right) corners of *inset_ax*. Two lines 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')
x, y, width, height = bounds
rectangle_patch = mpatches.Rectangle(
(x, y), width, height,
facecolor=facecolor, edgecolor=edgecolor, alpha=alpha,
zorder=zorder, label=label, transform=transform, **kwargs)
self.add_patch(rectangle_patch)
connects = []
if inset_ax is not None:
# connect the inset_axes to the rectangle
for xy_inset_ax in [(0, 0), (0, 1), (1, 0), (1, 1)]:
# inset_ax positions are in axes coordinates
# The 0, 1 values define the four edges if the inset_ax
# lower_left, upper_left, lower_right upper_right.
ex, ey = xy_inset_ax
if self.xaxis.get_inverted():
ex = 1 - ex
if self.yaxis.get_inverted():
ey = 1 - ey
xy_data = x + ex * width, y + ey * height
p = mpatches.ConnectionPatch(
xyA=xy_inset_ax, coordsA=inset_ax.transAxes,
xyB=xy_data, coordsB=self.transData,
arrowstyle="-", zorder=zorder,
edgecolor=edgecolor, alpha=alpha)
connects.append(p)
self.add_patch(p)
# 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 rectangle_patch, tuple(connects) if connects else None
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 keyword arguments are passed on to `.Axes.indicate_inset`
Returns
-------
rectangle_patch : `.Patches.Rectangle`
Rectangle artist.
connector_lines : 4-tuple of `.Patches.ConnectionPatch`
Each of four connector lines coming from the rectangle drawn on
this axis, in the order lower left, upper left, lower right,
upper right.
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])
return self.indicate_inset(rect, inset_ax, **kwargs)
@docstring.dedent_interpd
def secondary_xaxis(self, location, *, functions=None, **kwargs):
"""
Add a second x-axis to this axes.
For example if we want to have a second scale for the data plotted on
the xaxis.
%(_secax_docstring)s
Examples
--------
The main axis shows frequency, and the secondary axis shows period.
.. plot::
fig, ax = plt.subplots()
ax.loglog(range(1, 360, 5), range(1, 360, 5))
ax.set_xlabel('frequency [Hz]')
def invert(x):
return 1 / x
secax = ax.secondary_xaxis('top', functions=(invert, invert))
secax.set_xlabel('Period [s]')
plt.show()
"""
if (location in ['top', 'bottom'] or isinstance(location, Number)):
secondary_ax = SecondaryAxis(self, 'x', location, functions,
**kwargs)
self.add_child_axes(secondary_ax)
return secondary_ax
else:
raise ValueError('secondary_xaxis location must be either '
'a float or "top"/"bottom"')
Remove debug prints
2020-06-26 10:06:43 -04:00
@docstring.dedent_interpd
Add virtual environment to the git repository, this isn't 100% right but it's practical at this development point
2020-06-16 10:34:17 -04:00
def secondary_yaxis(self, location, *, functions=None, **kwargs):
"""
Add a second y-axis to this axes.
For example if we want to have a second scale for the data plotted on
the yaxis.
%(_secax_docstring)s
Examples
--------
Add a secondary axes that converts from radians to degrees
.. plot::
fig, ax = plt.subplots()
ax.plot(range(1, 360, 5), range(1, 360, 5))
ax.set_ylabel('degrees')
secax = ax.secondary_yaxis('right', functions=(np.deg2rad,
np.rad2deg))
secax.set_ylabel('radians')
"""
if location in ['left', 'right'] or isinstance(location, Number):
secondary_ax = SecondaryAxis(self, 'y', location,
functions, **kwargs)
self.add_child_axes(secondary_ax)
return secondary_ax
else:
raise ValueError('secondary_yaxis location must be either '
'a float or "left"/"right"')
@cbook._delete_parameter("3.1", "withdash")
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))
"""
if fontdict is None:
fontdict = {}
effective_kwargs = {
'verticalalignment': 'baseline',
'horizontalalignment': 'left',
'transform': self.transData,
'clip_on': False,
**fontdict,
**kwargs,
}
# 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
and withdash is not cbook.deprecation._deprecated_parameter):
t = mtext.TextWithDash(x, y, text=s)
else:
t = mtext.Text(x, y, text=s)
t.update(effective_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 : `~matplotlib.lines.Line2D`
Other Parameters
----------------
**kwargs
Valid keyword arguments are `.Line2D` properties, with the
exception of 'transform':
%(_Line2D_docstr)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._request_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 : `~matplotlib.lines.Line2D`
Other Parameters
----------------
**kwargs
Valid keyword arguments are `.Line2D` properties, with the
exception of 'transform':
%(_Line2D_docstr)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._request_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._request_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 `.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._request_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 : str, 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 np.iterable(y):
y = [y]
if not np.iterable(xmin):
xmin = [xmin]
if not np.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._request_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 : str, 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 np.iterable(x):
x = [x]
if not np.iterable(ymin):
ymin = [ymin]
if not np.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._request_autoscale_view()
return lines
@_preprocess_data(replace_names=["positions", "lineoffsets",
"linelengths", "linewidths",
"colors", "linestyles"])
@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 np.iterable(positions):
positions = [positions]
elif any(np.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 np.iterable(lineoffsets):
lineoffsets = [lineoffsets]
if not np.iterable(linelengths):
linelengths = [linelengths]
if not np.iterable(linewidths):
linewidths = [linewidths]
if not np.iterable(colors):
colors = [colors]
if hasattr(linestyles, 'lower') or not np.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._request_autoscale_view()
return colls
#### Basic plotting
# Uses a custom implementation of data-kwarg handling in
# _process_plot_var_args.
@docstring.dedent_interpd
def plot(self, *args, scalex=True, scaley=True, data=None, **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
:rc:`axes.prop_cycle`.
Parameters
----------
x, y : array-like or scalar
The horizontal / vertical coordinates of the data points.
*x* values are optional and default to `range(len(y))`.
Commonly, these parameters are 1D arrays.
They can also be scalars, or two-dimensional (in that case, the
columns represent separate data sets).
These arguments cannot be passed as keywords.
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.
This argument cannot be passed as keyword.
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_docstr)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 = '[marker][line][color]'
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.
Other combinations such as ``[color][marker][line]`` are also
supported, but note that their parsing may be ambiguous.
**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
'or' # red circles
'-g' # green solid line
'--' # dashed line with default color
'^k:' # black triangle_up markers connected by a dotted line
**Colors**
The supported color abbreviations are the single letter codes
============= ===============================
character color
============= ===============================
``'b'`` blue
``'g'`` green
``'r'`` red
``'c'`` cyan
``'m'`` magenta
``'y'`` yellow
``'k'`` black
``'w'`` white
============= ===============================
and the ``'CN'`` colors that index into the default property cycle.
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'``).
"""
kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)
lines = [*self._get_lines(*args, data=data, **kwargs)]
for line in lines:
self.add_line(line)
self._request_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 : timezone string or `tzinfo` or None
The time zone to use in labeling dates. If *None*, defaults to
:rc:`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 `.Line2D` properties:
%(_Line2D_docstr)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._request_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 : array-like
detrend : callable, optional, default: `mlab.detrend_none`
*x* is detrended by the *detrend* callable. This must be a
function ``x = detrend(x)`` accepting and returning an
`numpy.array`. Default is no normalization.
normed : bool, optional, default: True
If ``True``, input vectors are normalised to unit length.
usevlines : bool, optional, default: True
Determines the plot style.
If ``True``, vertical lines are plotted from 0 to the acorr value
using `Axes.vlines`. Additionally, a horizontal line is plotted
at y=0 using `Axes.axhline`.
If ``False``, markers are plotted at the acorr values using
`Axes.plot`.
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``)
The lag vector.
c : array (length ``2*maxlags+1``)
The 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
The linestyle for plotting the data points.
Only used if *usevlines* is ``False``.
marker : str, optional, default: 'o'
The marker for plotting the data points.
Only used if *usevlines* is ``False``.
Notes
-----
The cross correlation is performed with :func:`numpy.correlate` with
``mode = "full"``.
"""
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 : array-like of length n
y : array-like of length n
detrend : callable, optional, default: `mlab.detrend_none`
*x* and *y* are detrended by the *detrend* callable. This must be a
function ``x = detrend(x)`` accepting and returning an
`numpy.array`. Default is no normalization.
normed : bool, optional, default: True
If ``True``, input vectors are normalised to unit length.
usevlines : bool, optional, default: True
Determines the plot style.
If ``True``, vertical lines are plotted from 0 to the xcorr value
using `Axes.vlines`. Additionally, a horizontal line is plotted
at y=0 using `Axes.axhline`.
If ``False``, markers are plotted at the xcorr values using
`Axes.plot`.
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``)
The lag vector.
c : array (length ``2*maxlags+1``)
The 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
The linestyle for plotting the data points.
Only used if *usevlines* is ``False``.
marker : str, optional, default: 'o'
The marker for plotting the data points.
Only used if *usevlines* is ``False``.
Notes
-----
The cross correlation is performed with :func:`numpy.correlate` with
``mode = "full"``.
"""
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="full")
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() # let 'plot' do the unpacking..
def step(self, x, y, *args, where='pre', data=None, **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]
"""
cbook._check_in_list(('pre', 'post', 'mid'), where=where)
kwargs['drawstyle'] = 'steps-' + where
return self.plot(x, y, *args, data=data, **kwargs)
@staticmethod
def _convert_dx(dx, x0, xconv, convert):
"""
Small helper to do logic of width conversion flexibly.
*dx* and *x0* have units, but *xconv* has already been converted
to unitless (and is an ndarray). This allows the *dx* to have units
that are different from *x0*, but are still accepted by the
``__add__`` operator of *x0*.
"""
# x should be an array...
assert type(xconv) is np.ndarray
if xconv.size == 0:
# xconv has already been converted, but maybe empty...
return convert(dx)
try:
# attempt to add the width to x0; this works for
# datetime+timedelta, for instance
# only use the first element of x and x0. This saves
# having to be sure addition works across the whole
# vector. This is particularly an issue if
# x0 and dx are lists so x0 + dx just concatenates the lists.
# We can't just cast x0 and dx to numpy arrays because that
# removes the units from unit packages like `pint` that
# wrap numpy arrays.
try:
x0 = cbook.safe_first_element(x0)
except (TypeError, IndexError, KeyError):
x0 = x0
try:
x = cbook.safe_first_element(xconv)
except (TypeError, IndexError, KeyError):
x = xconv
delist = False
if not np.iterable(dx):
dx = [dx]
delist = True
dx = [convert(x0 + ddx) - x for ddx in dx]
if delist:
dx = dx[0]
except (ValueError, TypeError, AttributeError):
# if the above fails (for any reason) just fallback to what
# we do by default and convert dx by itself.
dx = convert(dx)
return dx
@_preprocess_data()
@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 : str 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)
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', {})
ezorder = error_kw.pop('zorder', None)
if ezorder is None:
ezorder = kwargs.get('zorder', None)
if ezorder is not None:
# If using the bar zorder, increment slightly to make sure
# errorbars are drawn on top of bars
ezorder += 0.01
error_kw.setdefault('zorder', ezorder)
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')
cbook._check_in_list(['vertical', 'horizontal'],
orientation=orientation)
log = kwargs.pop('log', False)
label = kwargs.pop('label', '')
tick_labels = kwargs.pop('tick_label', None)
y = bottom # Matches barh call signature.
if orientation == 'vertical':
if y is None:
y = 0
elif orientation == 'horizontal':
if x is None:
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')
# lets do some conversions now since some types cannot be
# subtracted uniformly
if self.xaxis is not None:
x0 = x
x = np.asarray(self.convert_xunits(x))
width = self._convert_dx(width, x0, x, self.convert_xunits)
if xerr is not None:
xerr = self._convert_dx(xerr, x0, x, self.convert_xunits)
if self.yaxis is not None:
y0 = y
y = np.asarray(self.convert_yunits(y))
height = self._convert_dx(height, y0, y, self.convert_yunits)
if yerr is not None:
yerr = self._convert_dx(yerr, y0, y, self.convert_yunits)
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
cbook._check_in_list(['center', 'edge'], align=align)
if align == 'center':
if orientation == 'vertical':
try:
left = x - width / 2
except TypeError as e:
raise TypeError(f'the dtypes of parameters x ({x.dtype}) '
f'and width ({width.dtype}) '
f'are incompatible') from e
bottom = y
elif orientation == 'horizontal':
try:
bottom = y - height / 2
except TypeError as e:
raise TypeError(f'the dtypes of parameters y ({y.dtype}) '
f'and height ({height.dtype}) '
f'are incompatible') from e
left = x
elif align == 'edge':
left = x
bottom = y
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
self._request_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 : str 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()
@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*.
yrange : (*ymin*, *yheight*)
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`
"""
# 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_conv = []
for xr in xranges:
if len(xr) != 2:
raise ValueError('each range in xrange must be a sequence '
'with two elements (i.e. an Nx2 array)')
# convert the absolute values, not the x and dx...
x_conv = np.asarray(self.convert_xunits(xr[0]))
x1 = self._convert_dx(xr[1], xr[0], x_conv, self.convert_xunits)
xranges_conv.append((x_conv, x1))
yrange_conv = self.convert_yunits(yrange)
col = mcoll.BrokenBarHCollection(xranges_conv, yrange_conv, **kwargs)
self.add_collection(col, autolim=True)
self._request_autoscale_view()
return col
@_preprocess_data()
def stem(self, *args, linefmt=None, markerfmt=None, basefmt=None, bottom=0,
label=None, use_line_collection=False):
"""
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.
use_line_collection : bool, optional, default: False
If ``True``, store and plot the stem lines as a
`~.collections.LineCollection` instead of individual lines. This
significantly increases performance, and will become the default
option in Matplotlib 3.3. If ``False``, defaults to the old
behavior of using a list of `.Line2D` objects.
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))
if len(args) == 1:
y, = args
x = np.arange(len(y))
args = ()
else:
x, y, *args = args
self._process_unit_info(xdata=x, ydata=y)
x = self.convert_xunits(x)
y = self.convert_yunits(y)
# 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)
# New behaviour in 3.1 is to use a LineCollection for the stemlines
if use_line_collection:
stemlines = [((xi, bottom), (xi, yi)) for xi, yi in zip(x, y)]
if linestyle is None:
linestyle = rcParams['lines.linestyle']
stemlines = mcoll.LineCollection(stemlines, linestyles=linestyle,
colors=linecolor,
label='_nolegend_')
self.add_collection(stemlines)
# Old behaviour is to plot each of the lines individually
else:
cbook._warn_external(
'In Matplotlib 3.3 individual lines on a stem plot will be '
'added as a LineCollection instead of individual lines. '
'This significantly improves the performance of a stem plot. '
'To remove this warning and switch to the new behaviour, '
'set the "use_line_collection" keyword argument to True.')
stemlines = []
for xi, yi in zip(x, y):
l, = self.plot([xi, xi], [bottom, yi],
color=linecolor, linestyle=linestyle,
marker=linemarker, label="_nolegend_")
stemlines.append(l)
markerline, = self.plot(x, y, color=markercolor, linestyle=markerstyle,
marker=markermarker, label="_nolegend_")
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"])
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), str, 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 or None, optional, default: 1.1
The radial distance at which the pie labels are drawn.
If set to ``None``, label are not drawn, but are stored for use in
``legend()``
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')
# The use of float32 is "historical", but can't be changed without
# regenerating the test baselines.
x = np.asarray(x, np.float32)
if x.ndim != 1 and x.squeeze().ndim <= 1:
cbook.warn_deprecated(
"3.1", message="Non-1D inputs to pie() are currently "
"squeeze()d, but this behavior is deprecated since %(since)s "
"and will be removed %(removal)s; pass a 1D array instead.")
x = np.atleast_1d(x.squeeze())
sx = x.sum()
if sx > 1:
x = 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 = []
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)
if labeldistance is not None:
xt = x + labeldistance * radius * math.cos(thetam)
yt = y + labeldistance * radius * math.sin(thetam)
label_alignment_h = 'left' if xt > 0 else 'right'
label_alignment_v = 'center'
label_rotation = 'horizontal'
if rotatelabels:
label_alignment_v = 'bottom' if yt > 0 else '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
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 : str, 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 : color, optional, default: None
The color of 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: False
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 : int or (int, int), optional, default: 1
draws error bars on a subset of the data. *errorevery* =N draws
error bars on the points (x[::N], y[::N]).
*errorevery* =(start, N) draws error bars on the points
(x[start::N], y[start::N]). e.g. errorevery=(6, 3)
adds error bars to the data at (x[6], x[9], x[12], x[15], ...).
Used to avoid overlapping error bars when two series share x-axis
values.
Returns
-------
container : :class:`~.container.ErrorbarContainer`
The container contains:
- plotline: `.Line2D` instance of x, y plot markers and/or line.
- caplines: A tuple of `.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_docstr)s
"""
kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)
# 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)
try:
offset, errorevery = errorevery
except TypeError:
offset = 0
if errorevery < 1 or int(errorevery) != errorevery:
raise ValueError(
'errorevery must be positive integer or tuple of integers')
if int(offset) != offset:
raise ValueError("errorevery's starting index must be an 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 np.iterable(x):
x = [x]
if not np.iterable(y):
y = [y]
if xerr is not None:
if not np.iterable(xerr):
xerr = [xerr] * len(x)
if yerr is not None:
if not np.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.zeros(len(x), bool)
everymask[offset::errorevery] = True
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 parse *err* and subtract/add it to *data*.
Both *err* and *data* are already iterables at this point.
"""
try: # Asymmetric error: pair of 1D iterables.
a, b = err
iter(a)
iter(b)
except (TypeError, ValueError):
a = b = err # Symmetric error: 1D iterable.
# This could just be `np.ndim(a) > 1 and np.ndim(b) > 1`, except
# for the (undocumented, but tested) support for (n, 1) arrays.
a_sh = np.shape(a)
b_sh = np.shape(b)
if (len(a_sh) > 2 or (len(a_sh) == 2 and a_sh[1] != 1)
or len(b_sh) > 2 or (len(b_sh) == 2 and b_sh[1] != 1)):
raise ValueError(
"err must be a scalar or a 1D or (2, n) array-like")
if len(a_sh) == 2 or len(b_sh) == 2:
cbook.warn_deprecated(
"3.1", message="Support for passing a (n, 1)-shaped error "
"array to errorbar() is deprecated since Matplotlib "
"%(since)s and will be removed %(removal)s; pass a 1D "
"array instead.")
# Using list comprehensions rather than arrays to preserve units.
for e in [a, b]:
if len(data) != len(e):
raise ValueError(
f"The lengths of the data ({len(data)}) and the "
f"error {len(e)} do not match")
low = [v - e for v, e in zip(data, a)]
high = [v + e for v, e in zip(data, b)]
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._request_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)
@cbook._rename_parameter("3.1", "manage_xticks", "manage_ticks")
@_preprocess_data()
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_ticks=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 or (float, float) (default = 1.5)
The position of the whiskers.
If a float, the lower whisker is at the lowest datum above
``Q1 - whis*(Q3-Q1)``, and the upper whisker at the highest datum
below ``Q3 + whis*(Q3-Q1)``, where Q1 and Q3 are the first and
third quartiles. The default value of ``whis = 1.5`` corresponds
to Tukey's original definition of boxplots.
If a pair of floats, they indicate the percentiles at which to
draw the whiskers (e.g., (5, 95)). In particular, setting this to
(0, 100) results in whiskers covering the whole range of the data.
"range" is a deprecated synonym for (0, 100).
In the edge case where ``Q1 == Q3``, *whis* is automatically set
to (0, 100) (cover the whole range of the data) if *autorange* is
True.
Beyond the whiskers, data are considered outliers and are plotted
as individual points.
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_ticks : bool, optional (True)
If True, the tick locations and labels will be adjusted to match
the boxplot positions.
autorange : bool, optional (False)
When `True` and the data are distributed such that the 25th and
75th percentiles are equal, ``whis`` is set to (0, 100) 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 `.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.
"""
# 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']
if boxprops is None:
boxprops = {}
if whiskerprops is None:
whiskerprops = {}
if capprops is None:
capprops = {}
if medianprops is None:
medianprops = {}
if meanprops is None:
meanprops = {}
if flierprops is None:
flierprops = {}
if patch_artist:
boxprops['linestyle'] = 'solid' # Not consistent with bxp.
if 'color' in boxprops:
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_ticks=manage_ticks, zorder=zorder)
return artists
@cbook._rename_parameter("3.1", "manage_xticks", "manage_ticks")
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_ticks=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 = True
If `True` (default), makes the boxes vertical. If `False`,
makes horizontal boxes.
patch_artist : bool, default = False
If `False` produces boxes with the `.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_ticks : bool, default = True
If True, the tick locations and labels will be adjusted to match the
boxplot positions.
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 `.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
def line_props_with_rcdefaults(subkey, explicit, zdelta=0):
d = {k.split('.')[-1]: v for k, v in rcParams.items()
if k.startswith(f'boxplot.{subkey}')}
d['zorder'] = zorder + zdelta
if explicit is not None:
d.update(
cbook.normalize_kwargs(explicit, mlines.Line2D._alias_map))
return d
# box properties
if patch_artist:
final_boxprops = dict(
linestyle=rcParams['boxplot.boxprops.linestyle'],
linewidth=rcParams['boxplot.boxprops.linewidth'],
edgecolor=rcParams['boxplot.boxprops.color'],
facecolor=('white' if rcParams['_internal.classic_mode'] else
rcParams['patch.facecolor']),
zorder=zorder,
)
if boxprops is not None:
final_boxprops.update(
cbook.normalize_kwargs(
boxprops, mpatches.PathPatch._alias_map))
else:
final_boxprops = line_props_with_rcdefaults('boxprops', boxprops)
final_whiskerprops = line_props_with_rcdefaults(
'whiskerprops', whiskerprops)
final_capprops = line_props_with_rcdefaults(
'capprops', capprops)
final_flierprops = line_props_with_rcdefaults(
'flierprops', flierprops)
final_medianprops = line_props_with_rcdefaults(
'medianprops', medianprops, zdelta)
final_meanprops = line_props_with_rcdefaults(
'meanprops', meanprops, zdelta)
removed_prop = 'marker' if meanline else 'linestyle'
# Only remove the property if it's not set explicitly as a parameter.
if meanprops is None or removed_prop not in meanprops:
final_meanprops[removed_prop] = ''
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"))
positions = np.array(positions)
if len(positions) > 0 and not isinstance(positions[0], Number):
raise TypeError("positions should be an iterable of numbers")
# 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.full(len(stats['fliers']), pos, dtype=np.float64)
flier_y = stats['fliers']
fliers.extend(doplot(
flier_x, flier_y, **final_flierprops
))
if manage_ticks:
axis_name = "x" if vert else "y"
interval = getattr(self.dataLim, f"interval{axis_name}")
axis = getattr(self, f"{axis_name}axis")
positions = axis.convert_units(positions)
# The 0.5 additional padding ensures reasonable-looking boxes
# even when drawing a single box. We set the sticky edge to
# prevent margins expansion, in order to match old behavior (back
# when separate calls to boxplot() would completely reset the axis
# limits regardless of what was drawn before). The sticky edges
# are attached to the median lines, as they are always present.
interval[:] = (min(interval[0], min(positions) - .5),
max(interval[1], max(positions) + .5))
for median, position in zip(medians, positions):
getattr(median.sticky_edges, axis_name).extend(
[position - .5, position + .5])
# Modified from Axis.set_ticks and Axis.set_ticklabels.
locator = axis.get_major_locator()
if not isinstance(axis.get_major_locator(),
mticker.FixedLocator):
locator = mticker.FixedLocator([])
axis.set_major_locator(locator)
locator.locs = np.array([*locator.locs, *positions])
formatter = axis.get_major_formatter()
if not isinstance(axis.get_major_formatter(),
mticker.FixedFormatter):
formatter = mticker.FixedFormatter([])
axis.set_major_formatter(formatter)
formatter.seq = [*formatter.seq, *datalabels]
self._request_autoscale_view(
scalex=self._autoscaleXon, scaley=self._autoscaleYon)
return dict(whiskers=whiskers, caps=caps, boxes=boxes,
medians=medians, fliers=fliers, means=means)
@staticmethod
def _parse_scatter_color_args(c, edgecolors, kwargs, xsize,
get_next_color_func):
"""
Helper function to process color related arguments of `.Axes.scatter`.
Argument precedence for facecolors:
- c (if not None)
- kwargs['facecolors']
- kwargs['facecolor']
- kwargs['color'] (==kwcolor)
- 'b' if in classic mode else the result of ``get_next_color_func()``
Argument precedence for edgecolors:
- edgecolors (is an explicit kw argument in scatter())
- kwargs['edgecolor']
- kwargs['color'] (==kwcolor)
- 'face' if not in classic mode else None
Parameters
----------
c : color or sequence or sequence of color or None
See argument description of `.Axes.scatter`.
edgecolors : color or sequence of color or {'face', 'none'} or None
See argument description of `.Axes.scatter`.
kwargs : dict
Additional kwargs. If these keys exist, we pop and process them:
'facecolors', 'facecolor', 'edgecolor', 'color'
Note: The dict is modified by this function.
xsize : int
The size of the x and y arrays passed to `.Axes.scatter`.
get_next_color_func : callable
A callable that returns a color. This color is used as facecolor
if no other color is provided.
Note, that this is a function rather than a fixed color value to
support conditional evaluation of the next color. As of the
current implementation obtaining the next color from the
property cycle advances the cycle. This must only happen if we
actually use the color, which will only be decided within this
method.
Returns
-------
c
The input *c* if it was not *None*, else a color derived from the
other inputs or defaults.
colors : array(N, 4) or None
The facecolors as RGBA values, or *None* if a colormap is used.
edgecolors
The edgecolor.
"""
facecolors = kwargs.pop('facecolors', None)
facecolors = kwargs.pop('facecolor', facecolors)
edgecolors = kwargs.pop('edgecolor', edgecolors)
kwcolor = kwargs.pop('color', None)
if kwcolor is not None and c is not None:
raise ValueError("Supply a 'c' argument or a 'color'"
" kwarg but not both; they differ but"
" their functionalities overlap.")
if kwcolor is not None:
try:
mcolors.to_rgba_array(kwcolor)
except ValueError:
raise ValueError(
"'color' kwarg must be an color or sequence of color "
"specs. For a sequence of values to be color-mapped, use "
"the 'c' argument instead.")
if edgecolors is None:
edgecolors = kwcolor
if facecolors is None:
facecolors = kwcolor
if edgecolors is None and not rcParams['_internal.classic_mode']:
edgecolors = rcParams['scatter.edgecolors']
c_was_none = c is None
if c is None:
c = (facecolors if facecolors is not None
else "b" if rcParams['_internal.classic_mode']
else get_next_color_func())
c_is_string_or_strings = (
isinstance(c, str)
or (isinstance(c, collections.abc.Iterable) and len(c) > 0
and isinstance(cbook.safe_first_element(c), str)))
def invalid_shape_exception(csize, xsize):
return ValueError(
f"'c' argument has {csize} elements, which is inconsistent "
f"with 'x' and 'y' with size {xsize}.")
c_is_mapped = False # Unless proven otherwise below.
valid_shape = True # Unless proven otherwise below.
if not c_was_none and kwcolor is None and not c_is_string_or_strings:
try: # First, does 'c' look suitable for value-mapping?
c = np.asanyarray(c, dtype=float)
except ValueError:
pass # Failed to convert to float array; must be color specs.
else:
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# handle the documented special case of a 2D array with 1
# row which as RGB(A) to broadcast.
if c.shape == (1, 4) or c.shape == (1, 3):
c_is_mapped = False
if c.size != xsize:
valid_shape = False
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# If c can be either mapped values or a RGB(A) color, prefer
# the former if shapes match, the latter otherwise.
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elif c.size == xsize:
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c = c.ravel()
c_is_mapped = True
else: # Wrong size; it must not be intended for mapping.
if c.shape in ((3,), (4,)):
_log.warning(
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"*c* argument looks like a single numeric RGB or "
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"RGBA sequence, which should be avoided as value-"
"mapping will have precedence in case its length "
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"matches with *x* & *y*. Please use the *color* "
"keyword-argument or provide a 2-D array "
"with a single row if you intend to specify "
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"the same RGB or RGBA value for all points.")
valid_shape = False
if not c_is_mapped:
try: # Is 'c' acceptable as PathCollection facecolors?
colors = mcolors.to_rgba_array(c)
except ValueError:
if not valid_shape:
raise invalid_shape_exception(c.size, xsize)
# Both the mapping *and* the RGBA conversion failed: pretty
# severe failure => one may appreciate a verbose feedback.
raise ValueError(
f"'c' argument must be a color, a sequence of colors, or "
f"a sequence of numbers, not {c}")
else:
if len(colors) not in (0, 1, xsize):
# NB: remember that a single color is also acceptable.
# Besides *colors* will be an empty array if c == 'none'.
raise invalid_shape_exception(len(colors), xsize)
else:
colors = None # use cmap, norm after collection is created
return c, colors, edgecolors
@_preprocess_data(replace_names=["x", "y", "s", "linewidths",
"edgecolors", "c", "facecolor",
"facecolors", "color"],
label_namer="y")
@cbook._delete_parameter("3.2", "verts")
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, *, plotnonfinite=False,
**kwargs):
"""
A scatter plot of *y* vs. *x* with varying marker size and/or color.
Parameters
----------
x, y : scalar or 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``.
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c : array-like or list of colors or color, optional
The marker colors. Possible values:
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- A scalar or 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.
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- A sequence of colors of length n.
- A single color format string.
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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*.
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If you wish to specify a single color for all points
prefer the *color* keyword argument.
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
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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 :rc:`lines.linewidth`.
edgecolors : {'face', 'none', *None*} or color or sequence of color, \
optional.
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 Matplotlib color or sequence of color.
Defaults to ``None``, in which case it takes the value of
:rc:`scatter.edgecolors` = 'face'.
For non-filled markers, the *edgecolors* kwarg is ignored and
forced to 'face' internally.
plotnonfinite : boolean, optional, default: False
Set to plot points with nonfinite *c*, in conjunction with
`~matplotlib.colors.Colormap.set_bad`.
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 N-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:
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.
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:
s = (20 if rcParams['_internal.classic_mode'] else
rcParams['lines.markersize'] ** 2.0)
s = np.ma.ravel(s)
if len(s) not in (1, x.size):
raise ValueError("s must be a scalar, or the same size as x and y")
c, colors, edgecolors = \
self._parse_scatter_color_args(
c, edgecolors, kwargs, x.size,
get_next_color_func=self._get_patches_for_fill.get_next_color)
if plotnonfinite and colors is None:
c = np.ma.masked_invalid(c)
x, y, s, edgecolors, linewidths = \
cbook._combine_masks(x, y, s, edgecolors, linewidths)
else:
x, y, s, c, colors, edgecolors, linewidths = \
cbook._combine_masks(
x, y, s, c, colors, edgecolors, linewidths)
scales = s # Renamed for readability below.
# 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.ma.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:
collection.set_array(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._request_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 2D hexagonal binning plot of points *x*, *y*.
If *C* is *None*, the value of the hexagon is determined by the number
of points in the hexagon. Otherwise, *C* specifies values at the
coordinate (x[i], y[i]). For each hexagon, these values are reduced
using *reduce_C_function*.
Parameters
----------
x, y : array-like
The data positions. *x* and *y* must be of the same length.
C : array-like, optional
If given, these values are accumulated in the bins. Otherwise,
every point has a value of 1. Must be of the same length as *x*
and *y*.
gridsize : int or (int, int), default: 100
If a single int, the number of hexagons in the *x*-direction.
The number of hexagons in the *y*-direction is chosen such that
the hexagons are approximately regular.
Alternatively, if a tuple (*nx*, *ny*), the number of hexagons
in the *x*-direction and the *y*-direction.
bins : 'log' or int or sequence, default: *None*
Discretization of the hexagon values.
- 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. This is equivalent to ``norm=LogNorm()``.
- 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'}, default: 'linear'
Use a linear or log10 scale on the horizontal axis.
yscale : {'linear', 'log'}, default: 'linear'
Use a linear or log10 scale on the vertical axis.
mincnt : int > 0, default: *None*
If not *None*, only display cells with more than *mincnt*
number of points in the cell.
marginals : bool, default: *False*
If marginals is *True*, plot the marginal density as
colormapped rectangles along the bottom of the x-axis and
left of the y-axis.
extent : float, default: *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 : str or `~matplotlib.colors.Colormap`, optional
The Colormap instance or registered colormap name used to map
the bin values to colors. Defaults to :rc:`image.cmap`.
norm : `~matplotlib.colors.Normalize`, optional
The Normalize instance scales the bin 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 : float, 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 the bins in case of the default
linear scaling). This is ignored if *norm* is given.
alpha : float between 0 and 1, optional
The alpha blending value, between 0 (transparent) and 1 (opaque).
linewidths : float, default: *None*
If *None*, defaults to 1.0.
edgecolors : {'face', 'none', *None*} or color, default: 'face'
The color of the hexagon edges. Possible values are:
- 'face': Draw the edges in the same color as the fill color.
- 'none': No edges are drawn. This can sometimes lead to unsightly
unpainted pixels between the hexagons.
- *None*: Draw outlines in the default color.
- An explicit matplotlib color.
reduce_C_function : callable, default is `numpy.mean`
The function to aggregate *C* within the bins. It is ignored if
*C* is not given. This must have the signature::
def reduce_C_function(C: array) -> float
Commonly used functions are:
- `numpy.mean`: average of the points
- `numpy.sum`: integral of the point values
- `numpy.max`: value taken from the largest point
**kwargs : `~matplotlib.collections.PolyCollection` properties
All other keyword arguments are passed on to `.PolyCollection`:
%(PolyCollection)s
Returns
-------
polycollection : `~matplotlib.collections.PolyCollection`
A `.PolyCollection` defining the hexagonal bins.
- `.PolyCollection.get_offset` contains a Mx2 array containing
the x, y positions of the M hexagon centers.
- `.PolyCollection.get_array` contains the values of the M
hexagons.
If *marginals* is *True*, horizontal
bar and vertical bar (both PolyCollections) will be attached
to the return collection as attributes *hbar* and *vbar*.
"""
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 np.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))
c1 = (0 <= ix1) & (ix1 < nx1) & (0 <= iy1) & (iy1 < ny1) & bdist
c2 = (0 <= ix2) & (ix2 < nx2) & (0 <= iy2) & (iy2 < ny2) & ~bdist
np.add.at(lattice1, (ix1[c1], iy1[c1]), 1)
np.add.at(lattice2, (ix2[c2], iy2[c2]), 1)
if mincnt is not None:
lattice1[lattice1 < mincnt] = np.nan
lattice2[lattice2 < mincnt] = np.nan
accum = np.concatenate([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 = [sx, sy / 3] * np.array(
[[.5, -.5], [.5, .5], [0., 1.], [-.5, .5], [-.5, -.5], [0., -1.]])
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"
)
# Set normalizer if bins is 'log'
if bins == 'log':
if norm is not None:
cbook._warn_external("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 np.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)
self._request_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 and y coordinates of the arrow base.
dx, dy : float
The length of the arrow along x and 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
@docstring.copy(mquiver.QuiverKey.__init__)
def quiverkey(self, Q, X, Y, U, label, **kw):
qk = mquiver.QuiverKey(Q, X, Y, U, label, **kw)
self.add_artist(qk)
return qk
# 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()
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._request_autoscale_view()
return q
quiver.__doc__ = mquiver.Quiver.quiver_doc
# args can be some combination of X, Y, U, V, C and all should be replaced
@_preprocess_data()
@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._request_autoscale_view()
return b
# Uses a custom implementation of data-kwarg handling in
# _process_plot_var_args.
def fill(self, *args, data=None, **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
data : indexable object, optional
An object with labelled data. If given, provide the label names to
plot in *x* and *y*, e.g.::
ax.fill("time", "signal",
data={"time": [0, 1, 2], "signal": [0, 1, 0]})
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)
# _get_patches_for_fill returns a generator, convert it to a list.
patches = [*self._get_patches_for_fill(*args, data=data, **kwargs)]
for poly in patches:
self.add_patch(poly)
self._request_autoscale_view()
return patches
@_preprocess_data(replace_names=["x", "y1", "y2", "where"])
@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 relevant 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)
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
else:
where = np.asarray(where, dtype=bool)
if where.size != x.size:
cbook.warn_deprecated(
"3.2",
message="The parameter where must have the same size as x "
"in fill_between(). This will become an error in "
"future versions of Matplotlib.")
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 = cbook.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._request_autoscale_view()
return collection
@_preprocess_data(replace_names=["y", "x1", "x2", "where"])
@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 relevant 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 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 *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)
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
else:
where = np.asarray(where, dtype=bool)
if where.size != y.size:
cbook.warn_deprecated(
"3.2",
message="The parameter where must have the same size as y "
"in fill_between(). This will become an error in "
"future versions of Matplotlib.")
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 = cbook.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._request_autoscale_view()
return collection
#### plotting z(x, y): imshow, pcolor and relatives, contour
@_preprocess_data()
@cbook._delete_parameter("3.1", "shape")
@cbook._delete_parameter("3.1", "imlim")
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 data as an image; i.e. on a 2D regular raster.
The input may either be actual RGB(A) data, or 2D scalar data, which
will be rendered as a pseudocolor image. Note: For actually displaying
a grayscale image set up the color mapping using the parameters
``cmap='gray', vmin=0, vmax=255``.
Parameters
----------
X : array-like or PIL image
The image data. Supported array shapes are:
- (M, N): an image with scalar data. The values are mapped to
colors using normalization and a colormap. See parameters *norm*,
*cmap*, *vmin*, *vmax*.
- (M, N, 3): an image with RGB values (0-1 float or 0-255 int).
- (M, N, 4): an image with RGBA values (0-1 float or 0-255 int),
i.e. including transparency.
The first two dimensions (M, N) define the rows and columns of
the image.
Out-of-range RGB(A) values are clipped.
cmap : str or `~matplotlib.colors.Colormap`, optional
The Colormap instance or registered colormap name used to map
scalar data to colors. This parameter is ignored for RGB(A) data.
Defaults to :rc:`image.cmap`.
norm : `~matplotlib.colors.Normalize`, optional
The `Normalize` instance used to scale scalar data to the [0, 1]
range before mapping to colors using *cmap*. By default, a linear
scaling mapping the lowest value to 0 and the highest to 1 is used.
This parameter is ignored for RGB(A) data.
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`.
interpolation : str, optional
The interpolation method used. If *None*, :rc:`image.interpolation`
is used.
Supported values are 'none', 'antialiased', '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, pdf and svg backends. Other backends will fall back
to 'nearest'. Note that most SVG renders perform interpolation at
rendering and that the default interpolation method they implement
may differ.
If *interpolation* is the default 'antialiased', then 'nearest'
interpolation is used if the image is upsampled by more than a
factor of three (i.e. the number of display pixels is at least
three times the size of the data array). If the upsampling rate is
smaller than 3, or the image is downsampled, then 'hanning'
interpolation is used to act as an anti-aliasing filter, unless the
image happens to be upsampled by exactly a factor of two or one.
See
:doc:`/gallery/images_contours_and_fields/interpolation_methods`
for an overview of the supported interpolation methods, and
:doc:`/gallery/images_contours_and_fields/image_antialiasing` for
a discussion of image antialiasing.
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*.
alpha : scalar or array-like, optional
The alpha blending value, between 0 (transparent) and 1 (opaque).
If *alpha* is an array, the alpha blending values are applied pixel
by pixel, and *alpha* must have the same shape as *X*.
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.
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'.
See the :doc:`/tutorials/intermediate/imshow_extent` tutorial for
examples and a more detailed description.
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 :doc:`/tutorials/intermediate/imshow_extent` tutorial for
examples and a more detailed description.
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 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 Gouraud 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])
nrows, ncols = C.shape
if allmatch:
X, Y = np.meshgrid(np.arange(ncols), np.arange(nrows))
else:
X, Y = np.meshgrid(np.arange(ncols + 1), np.arange(nrows + 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
nrows, ncols = 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) != (ncols, nrows):
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 (ncols in (Nx, Nx - 1) and nrows 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()
@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.
- A 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`.
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
unmask = ~mask
X1 = ma.filled(X[:-1, :-1])[unmask]
Y1 = ma.filled(Y[:-1, :-1])[unmask]
X2 = ma.filled(X[1:, :-1])[unmask]
Y2 = ma.filled(Y[1:, :-1])[unmask]
X3 = ma.filled(X[1:, 1:])[unmask]
Y3 = ma.filled(Y[1:, 1:])[unmask]
X4 = ma.filled(X[:-1, 1:])[unmask]
Y4 = ma.filled(Y[:-1, 1:])[unmask]
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 = ma.filled(C[:Ny - 1, :Nx - 1])[unmask]
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)
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._request_autoscale_view()
return collection
@_preprocess_data()
@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::
`~Axes.pcolormesh` is similar to `~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.
- A 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)
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._request_autoscale_view()
return collection
@_preprocess_data()
@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 signature::
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)
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 (0-1 float or 0-255 int).
- (M, N, 4): an image with RGBA values (0-1 float or 0-255 int),
i.e. including transparency.
The first two dimensions (M, N) define the rows and columns of
the image.
This parameter can only be passed positionally.
X, Y : tuple or array-like, default: ``(0, N)``, ``(0, M)``
*X* and *Y* are used to specify the coordinates of the
quadrilaterals. There are different ways to do this:
- Use tuples ``X=(xmin, xmax)`` and ``Y=(ymin, ymax)`` to define
a *uniform rectangular 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 + 1, N + 1),
specifying the x and y coordinates of the corners of the colored
quadrilaterals.
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.
These arguments can only be passed positionally.
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]
"""
C = args[-1]
nr, nc = np.shape(C)[:2]
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":
# data point in each cell is value at lower left corner
coords = np.stack([x, y], axis=-1)
if np.ndim(C) == 2:
qm_kwargs = {"array": np.ma.ravel(C)}
elif np.ndim(C) == 3:
qm_kwargs = {"color": np.ma.reshape(C, (-1, C.shape[-1]))}
else:
raise ValueError("C must be 2D or 3D")
collection = mcoll.QuadMesh(
nc, nr, coords, **qm_kwargs,
alpha=alpha, cmap=cmap, norm=norm,
antialiased=False, edgecolors="none")
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.
extent = xl, xr, yb, yt = x[0], x[-1], y[0], y[-1]
if style == "image":
im = mimage.AxesImage(
self, cmap, norm,
data=C, alpha=alpha, extent=extent,
interpolation='nearest', origin='lower',
**kwargs)
elif style == "pcolorimage":
im = mimage.PcolorImage(
self, x, y, C,
cmap=cmap, norm=norm, alpha=alpha, extent=extent,
**kwargs)
self.add_image(im)
ret = im
if vmin is not None or vmax is not None:
ret.set_clim(vmin, vmax)
elif np.ndim(C) == 2: # C.ndim == 3 is RGB(A) so doesn't need scaling.
ret.autoscale_None()
if ret.get_clip_path() is None:
# image does not already have clipping set, clip to axes patch
ret.set_clip_path(self.patch)
ret.sticky_edges.x[:] = [xl, xr]
ret.sticky_edges.y[:] = [yb, yt]
self.update_datalim(np.array([[xl, yb], [xr, yt]]))
self._request_autoscale_view(tight=True)
return ret
@_preprocess_data()
def contour(self, *args, **kwargs):
kwargs['filled'] = False
contours = mcontour.QuadContourSet(self, *args, **kwargs)
self._request_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._request_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__
#### Data analysis
@_preprocess_data(replace_names=["x", 'weights'], label_namer="x")
def hist(self, x, bins=None, range=None, density=False, weights=None,
cumulative=False, bottom=None, histtype='bar', align='mid',
orientation='vertical', rwidth=None, log=False,
color=None, label=None, stacked=False, **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. See the
documentation of the *weights* parameter to draw a histogram of
already-binned 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.
The *bins*, *range*, *weights*, and *density* parameters behave as in
`numpy.histogram`.
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 *bins* is an integer, it defines the number of equal-width bins
in the range.
If *bins* is a sequence, it defines the bin edges, including the
left edge of the first bin and the right edge of the last bin;
in this case, bins may be unequally spaced. 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.
If *bins* is a string, it is one of the binning strategies
supported by `numpy.histogram_bin_edges`: 'auto', 'fd', 'doane',
'scott', 'stone', 'rice', 'sturges', or 'sqrt'.
The default is :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 ``False``.
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``.
This parameter can be used to draw a histogram of data that has
already been binned, e.g. using `np.histogram` (by treating each
bin as a single point with a weight equal to its count) ::
counts, bins = np.histogram(data)
plt.hist(bins[:-1], bins, weights=counts)
(or you may alternatively use `~.bar()`).
cumulative : bool or -1, 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 *density* is also ``True`` then the histogram is normalized such
that the last bin equals 1.
If *cumulative* is a number less than 0 (e.g., -1), the direction
of accumulation is reversed. In this case, if *density* is also
``True``, then the histogram is normalized such that the first bin
equals 1.
bottom : array-like, scalar, or None, default: None
Location of the bottom of each bin, ie. bins are drawn from
``bottom`` to ``bottom + hist(x, bins)`` If a scalar, the bottom
of 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.
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 or sequence of colors, 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``
Returns
-------
n : array or list of arrays
The values of the histogram bins. See *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. The dtype of the array *n* (or of its element arrays) will
always be float even if no weighting or normalization is used.
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
"""
# 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 to avoid cluttering subsequent code.
cbook._check_in_list(['bar', 'barstacked', 'step', 'stepfilled'],
histtype=histtype)
cbook._check_in_list(['left', 'mid', 'right'], align=align)
cbook._check_in_list(['horizontal', 'vertical'],
orientation=orientation)
if histtype == 'barstacked' and not stacked:
stacked = True
# basic input validation
input_empty = np.size(x) == 0
# Massage 'x' for processing.
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)
if not cbook.is_scalar_or_string(bins):
bins = self.convert_xunits(bins)
# 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)
hist_kwargs = dict()
# if the bin_range is not given, compute without nan numpy
# does not do this for us when guessing the range (but will
# happily ignore nans when computing the histogram).
if bin_range is None:
xmin = np.inf
xmax = -np.inf
for xi in x:
if len(xi):
# python's min/max ignore nan,
# np.minnan returns nan for all nan input
xmin = min(xmin, np.nanmin(xi))
xmax = max(xmax, np.nanmax(xi))
# make sure we have seen at least one non-nan and finite
# value before we reset the bin range
if not np.isnan([xmin, xmax]).any() and not (xmin > xmax):
bin_range = (xmin, xmax)
# 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 input_empty and len(x) > 1:
if weights is not None:
_w = np.concatenate(w)
else:
_w = None
bins = _histogram_bin_edges(np.concatenate(x), bins, bin_range, _w)
else:
hist_kwargs['range'] = bin_range
density = bool(density)
if density and not stacked:
hist_kwargs['density'] = density
# List to store all the top coordinates of the histograms
tops = [] # Will have shape (n_datasets, n_bins).
# 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)
tops.append(m)
tops = np.array(tops, float) # causes problems later if it's an int
if stacked:
tops = tops.cumsum(axis=0)
# If a stacked density plot, normalize so the area of all the
# stacked histograms together is 1
if density:
tops = (tops / np.diff(bins)) / tops[-1].sum()
if cumulative:
slc = slice(None)
if isinstance(cumulative, Number) and cumulative < 0:
slc = slice(None, None, -1)
if density:
tops = (tops * np.diff(bins))[:, slc].cumsum(axis=1)[:, slc]
else:
tops = tops[:, slc].cumsum(axis=1)[:, slc]
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':
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')
else: # orientation == 'vertical'
self.set_yscale('log', nonposy='clip')
if align == 'left':
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 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(0)
elif orientation == 'horizontal':
patch.sticky_edges.x.append(0)
# 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._request_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"])
@cbook._rename_parameter("3.1", "normed", "density")
def hist2d(self, x, y, bins=10, range=None, density=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.
density : bool, optional, default: False
Normalize histogram. *normed* is a deprecated synonym for this
parameter.
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 (set
to NaN before passing to imshow) 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 NaN 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 its 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=density, 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"])
@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.
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 : `~matplotlib.lines.Line2D`
The line created by this function.
Only returned if *return_line* is True.
Other Parameters
----------------
**kwargs
Keyword arguments control the `.Line2D` properties:
%(_Line2D_docstr)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):
r"""
Plot the cross-spectral density.
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 : `~matplotlib.lines.Line2D`
The line created by this function.
Only returned if *return_line* is True.
Other Parameters
----------------
**kwargs
Keyword arguments control the `.Line2D` properties:
%(_Line2D_docstr)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"])
@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.
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 : `~matplotlib.lines.Line2D`
The line created by this function.
Other Parameters
----------------
**kwargs
Keyword arguments control the `.Line2D` properties:
%(_Line2D_docstr)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.
"""
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"])
@docstring.dedent_interpd
def angle_spectrum(self, x, Fs=None, Fc=None, window=None,
pad_to=None, sides=None, **kwargs):
"""
Plot the angle spectrum.
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 : `~matplotlib.lines.Line2D`
The line created by this function.
Other Parameters
----------------
**kwargs
Keyword arguments control the `.Line2D` properties:
%(_Line2D_docstr)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.
"""
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"])
@docstring.dedent_interpd
def phase_spectrum(self, x, Fs=None, Fc=None, window=None,
pad_to=None, sides=None, **kwargs):
"""
Plot the phase spectrum.
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 : `~matplotlib.lines.Line2D`
The line created by this function.
Other Parameters
----------------
**kwargs
Keyword arguments control the `.Line2D` properties:
%(_Line2D_docstr)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.
"""
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"])
@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):
r"""
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 `.Line2D` properties:
%(_Line2D_docstr)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"])
@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.
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. '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 keyword arguments 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 keyword arguments are passed to this method.
**Marker style**
If *Z* is a `scipy.sparse.spmatrix` or *marker* or *markersize* are
*None*, a `.Line2D` object will be returned with the value of marker
determining the marker type, and any remaining keyword arguments
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'
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_docstr)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')
if 'interpolation' in kwargs:
raise TypeError(
"spy() got an unexpected keyword argument 'interpolation'")
ret = self.imshow(mask, interpolation='nearest', aspect=aspect,
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
if 'linestyle' in kwargs:
raise TypeError(
"spy() got an unexpected keyword argument 'linestyle'")
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 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)
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"])
def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
showmeans=False, showextrema=True, showmedians=False,
quantiles=None, 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, the maximum, and user-specified quantiles.
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.
quantiles : array-like, default = None
If not None, set a list of floats in interval [0, 1] for each violin,
which stands for the quantiles that will be rendered for that
violin.
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 `~.collections.PolyCollection`
instances containing the filled area of each violin.
- ``cmeans``: A `~.collections.LineCollection` instance that marks
the mean values of each of the violin's distribution.
- ``cmins``: A `~.collections.LineCollection` instance that marks
the bottom of each violin's distribution.
- ``cmaxes``: A `~.collections.LineCollection` instance that marks
the top of each violin's distribution.
- ``cbars``: A `~.collections.LineCollection` instance that marks
the centers of each violin's distribution.
- ``cmedians``: A `~.collections.LineCollection` instance that
marks the median values of each of the violin's distribution.
- ``cquantiles``: A `~.collections.LineCollection` instance created
to identify the quantile values of each of the violin's
distribution.
"""
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,
quantiles=quantiles)
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, the maximum, and the quantiles values.
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.
Optional keys are:
- ``quantiles``: A list of scalars containing the quantile values
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 vertically.
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 `~.collections.PolyCollection`
instances containing the filled area of each violin.
- ``cmeans``: A `~.collections.LineCollection` instance that marks
the mean values of each of the violin's distribution.
- ``cmins``: A `~.collections.LineCollection` instance that marks
the bottom of each violin's distribution.
- ``cmaxes``: A `~.collections.LineCollection` instance that marks
the top of each violin's distribution.
- ``cbars``: A `~.collections.LineCollection` instance that marks
the centers of each violin's distribution.
- ``cmedians``: A `~.collections.LineCollection` instance that
marks the median values of each of the violin's distribution.
- ``cquantiles``: A `~.collections.LineCollection` instance created
to identify the quantiles values of each of the violin's
distribution.
"""
# Statistical quantities to be plotted on the violins
means = []
mins = []
maxes = []
medians = []
quantiles = np.asarray([])
# 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'])
q = stats.get('quantiles')
if q is not None:
# If exist key quantiles, assume it's a list of floats
quantiles = np.concatenate((quantiles, q))
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)
# Render quantile values
if quantiles.size > 0:
# Recalculate ranges for statistics lines for quantiles.
# ppmins are the left end of quantiles lines
ppmins = np.asarray([])
# pmaxes are the right end of quantiles lines
ppmaxs = np.asarray([])
for stats, cmin, cmax in zip(vpstats, pmins, pmaxes):
q = stats.get('quantiles')
if q is not None:
ppmins = np.concatenate((ppmins, [cmin] * np.size(q)))
ppmaxs = np.concatenate((ppmaxs, [cmax] * np.size(q)))
# Start rendering
artists['cquantiles'] = perp_lines(quantiles, ppmins, ppmaxs,
colors=edgecolor)
return artists
# Methods that are entirely implemented in other modules.
table = mtable.table
# args can by either Y or y1, y2, ... and all should be replaced
stackplot = _preprocess_data()(mstack.stackplot)
streamplot = _preprocess_data(
replace_names=["x", "y", "u", "v", "start_points"])(mstream.streamplot)
tricontour = mtri.tricontour
tricontourf = mtri.tricontourf
tripcolor = mtri.tripcolor
triplot = mtri.triplot
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