::: redirect-from
/users/prev_whats_new/whats_new_3.3.0
:::

# What\'s new in Matplotlib 3.3.0 (Jul 16, 2020) {#whats-new-3-3-0}

For a list of all of the issues and pull requests since the last
revision, see the `github-stats`{.interpreted-text role="ref"}.

::: {.contents depth="4"}
Table of Contents
:::

::: {.toctree maxdepth="4"}
:::

## Figure and Axes creation / management

### Provisional API for composing semantic axes layouts from text or nested lists

The [.Figure]{.title-ref} class has a provisional method to generate
complex grids of named [.axes.Axes]{.title-ref} based on nested list
input or ASCII art:

::: {.plot include-source=""}

axd = plt.figure(constrained_layout=True).subplot_mosaic(

:   

    \[\[\'.\', \'histx\'\],

    :   \[\'histy\', \'scat\'\]\]

) for k, ax in axd.items(): ax.text(0.5, 0.5, k, ha=\'center\',
va=\'center\', fontsize=36, color=\'darkgrey\')
:::

or as a string (with single-character Axes labels):

::: {.plot include-source=""}

axd = plt.figure(constrained_layout=True).subplot_mosaic(

:   \"\"\" TTE L.E \"\"\")

for k, ax in axd.items():

:   

    ax.text(0.5, 0.5, k,

    :   ha=\'center\', va=\'center\', fontsize=36, color=\'darkgrey\')
:::

See `mosaic`{.interpreted-text role="ref"} for more details and
examples.

### `GridSpec.subplots()`

The [.GridSpec]{.title-ref} class gained a
[\~.GridSpecBase.subplots]{.title-ref} method, so that one can write :

    fig.add_gridspec(2, 2, height_ratios=[3, 1]).subplots()

as an alternative to :

    fig.subplots(2, 2, gridspec_kw={"height_ratios": [3, 1]})

### New `Axes.sharex`, `Axes.sharey` methods

These new methods allow sharing axes *immediately* after creating them.
Note that behavior is indeterminate if axes are not shared immediately
after creation.

For example, they can be used to selectively link some axes created all
together using \`\~.Figure.subplot_mosaic\`:

    fig = plt.figure(constrained_layout=True)
    axd = fig.subplot_mosaic([['.', 'histx'], ['histy', 'scat']],
                             gridspec_kw={'width_ratios': [1, 7],
                                          'height_ratios': [2, 7]})

    axd['histx'].sharex(axd['scat'])
    axd['histy'].sharey(axd['scat'])

::: plot
np.random.seed(0) x = np.random.random(100) \* 100 + 20 y =
np.random.random(100) \* 50 + 25 c = np.random.random(100) - 0.5

fig = plt.figure(constrained_layout=True) axd =
fig.subplot_mosaic(\[\[\'.\', \'histx\'\], \[\'histy\', \'scat\'\]\],
gridspec_kw={\'width_ratios\': \[1, 7\], \'height_ratios\': \[2, 7\]})

axd\[\'histy\'\].invert_xaxis() axd\[\'histx\'\].sharex(axd\[\'scat\'\])
axd\[\'histy\'\].sharey(axd\[\'scat\'\])

im = axd\[\'scat\'\].scatter(x, y, c=c, cmap=\'RdBu\', picker=True)
fig.colorbar(im, orientation=\'horizontal\', ax=axd\[\'scat\'\],
shrink=0.8)

axd\[\'histx\'\].hist(x) axd\[\'histy\'\].hist(y,
orientation=\'horizontal\')
:::

### tight_layout now supports suptitle

Previous versions did not consider [.Figure.suptitle]{.title-ref}, so it
may overlap with other artists after calling \`\~.Figure.tight_layout\`:

::: plot
fig, axs = plt.subplots(1, 3) for i, ax in enumerate(axs): ax.plot(\[1,
2, 3\]) ax.set_title(f\'Axes {i}\')

t = fig.suptitle(\'suptitle\') t.set_in_layout(False) fig.tight_layout()
:::

From now on, the `suptitle` will be considered:

::: plot
fig, axs = plt.subplots(1, 3) for i, ax in enumerate(axs): ax.plot(\[1,
2, 3\]) ax.set_title(f\'Axes {i}\')

fig.suptitle(\'suptitle\') fig.tight_layout()
:::

### Setting axes box aspect

It is now possible to set the aspect of an axes box directly via
[\~.Axes.set_box_aspect]{.title-ref}. The box aspect is the ratio
between axes height and axes width in physical units, independent of the
data limits. This is useful to, e.g., produce a square plot, independent
of the data it contains, or to have a non-image plot with the same axes
dimensions next to an image plot with fixed (data-)aspect.

For use cases check out the `Axes box aspect
</gallery/subplots_axes_and_figures/axes_box_aspect>`{.interpreted-text
role="doc"} example.

## Colors and colormaps

### Turbo colormap

Turbo is an improved rainbow colormap for visualization, created by the
Google AI team for computer vision and machine learning. Its purpose is
to display depth and disparity data. Please see the [Google AI
Blog](https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html)
for further details.

::: plot
gradient = np.linspace(0, 1, 256) gradient = np.vstack((gradient,
gradient)) cmaps = \[\'turbo\', \'jet\', \'gist_rainbow_r\', \'hsv_r\'\]

fig, axs = plt.subplots(len(cmaps), constrained_layout=True) for name,
ax in zip(cmaps, axs): ax.imshow(gradient, aspect=\'auto\',
cmap=plt.get_cmap(name)) ax.set_title(name) ax.set_axis_off()
:::

### `colors.BoundaryNorm` supports *extend* keyword argument

[\~.colors.BoundaryNorm]{.title-ref} now has an *extend* keyword
argument, analogous to *extend* in [\~.axes.Axes.contourf]{.title-ref}.
When set to \'both\', \'min\', or \'max\', it maps the corresponding
out-of-range values to [\~.colors.Colormap]{.title-ref} lookup-table
indices near the appropriate ends of their range so that the colors for
out-of range values are adjacent to, but distinct from, their in-range
neighbors. The colorbar inherits the *extend* argument from the norm, so
with `extend='both'`, for example, the colorbar will have triangular
extensions for out-of-range values with colors that differ from adjacent
in-range colors.

::: plot
import matplotlib.pyplot as plt from matplotlib.colors import
BoundaryNorm import numpy as np

\# Make the data dx, dy = 0.05, 0.05 y, x = np.mgrid\[slice(1, 5 + dy,
dy), slice(1, 5 + dx, dx)\] z = np.sin(x) \*\* 10 + np.cos(10 + y \* x)
\* np.cos(x) z = z\[:-1, :-1\]

\# Z roughly varies between -1 and +1. \# Color boundary levels range
from -0.8 to 0.8, so there are out-of-bounds \# areas. levels = \[-0.8,
-0.5, -0.2, 0.2, 0.5, 0.8\] cmap = plt.get_cmap(\'PiYG\')

fig, axs = plt.subplots(nrows=2, constrained_layout=True, sharex=True)

\# Before this change: norm = BoundaryNorm(levels, ncolors=cmap.N) im =
axs\[0\].pcolormesh(x, y, z, cmap=cmap, norm=norm) fig.colorbar(im,
ax=axs\[0\], extend=\'both\') axs\[0\].axis(\[x.min(), x.max(), y.min(),
y.max()\]) axs\[0\].set_title(\"Colorbar with extend=\'both\'\")

\# With the new keyword: norm = BoundaryNorm(levels, ncolors=cmap.N,
extend=\'both\') im = axs\[1\].pcolormesh(x, y, z, cmap=cmap, norm=norm)
fig.colorbar(im, ax=axs\[1\]) \# note that the colorbar is updated
accordingly axs\[1\].axis(\[x.min(), x.max(), y.min(), y.max()\])
axs\[1\].set_title(\"BoundaryNorm with extend=\'both\'\")

plt.show()
:::

### Text color for legend labels

The text color of legend labels can now be set by passing a parameter
`labelcolor` to [\~.axes.Axes.legend]{.title-ref}. The `labelcolor`
keyword can be:

-   A single color (either a string or RGBA tuple), which adjusts the
    text color of all the labels.
-   A list or tuple, allowing the text color of each label to be set
    individually.
-   `linecolor`, which sets the text color of each label to match the
    corresponding line color.
-   `markerfacecolor`, which sets the text color of each label to match
    the corresponding marker face color.
-   `markeredgecolor`, which sets the text color of each label to match
    the corresponding marker edge color.

::: plot
options = \[\'C3\', \'linecolor\', \'markerfacecolor\',
\'markeredgecolor\'\]

fig, axs = plt.subplots(2, 2, constrained_layout=True) for ax, color in
zip(axs.flat, options): ax.plot(\[1, 2, 3\], marker=\'o\', color=\'C0\',
markerfacecolor=\'C1\', markeredgecolor=\'C2\', linewidth=3,
markersize=10, markeredgewidth=3, label=\'a line\')

> ax.legend(labelcolor=color) ax.set_title(f\'labelcolor={color!r}\')
>
> ax.margins(0.1)
:::

### Pcolor and Pcolormesh now accept `shading='nearest'` and `'auto'`

Previously [.axes.Axes.pcolor]{.title-ref} and
[.axes.Axes.pcolormesh]{.title-ref} handled the situation where *x* and
*y* have the same (respective) size as *C* by dropping the last row and
column of *C*, and *x* and *y* are regarded as the edges of the
remaining rows and columns in *C*. However, many users want *x* and *y*
centered on the rows and columns of *C*.

To accommodate this, `shading='nearest'` and `shading='auto'` are new
allowed strings for the *shading* keyword argument. `'nearest'` will
center the color on *x* and *y* if *x* and *y* have the same dimensions
as *C* (otherwise an error will be thrown). `shading='auto'` will choose
\'flat\' or \'nearest\' based on the size of *X*, *Y*, *C*.

If `shading='flat'` then *X*, and *Y* should have dimensions one larger
than *C*. If *X* and *Y* have the same dimensions as *C*, then the
previous behavior is used and the last row and column of *C* are
dropped, and a DeprecationWarning is emitted.

Users can also specify this by the new
`pcolor.shading`{.interpreted-text role="rc"} in their `.matplotlibrc`
or via [.rcParams]{.title-ref}.

See
`pcolormesh </gallery/images_contours_and_fields/pcolormesh_grids>`{.interpreted-text
role="doc"} for examples.

## Titles, ticks, and labels

### Align labels to Axes edges

[\~.axes.Axes.set_xlabel]{.title-ref},
[\~.axes.Axes.set_ylabel]{.title-ref} and `ColorbarBase.set_label`
support a parameter `loc` for simplified positioning. For the xlabel,
the supported values are \'left\', \'center\', or \'right\'. For the
ylabel, the supported values are \'bottom\', \'center\', or \'top\'.

The default is controlled via `xaxis.labellocation`{.interpreted-text
role="rc"} and `yaxis.labellocation`{.interpreted-text role="rc"}; the
Colorbar label takes the rcParam based on its orientation.

::: plot
options = \[\'left\', \'center\', \'right\'\] fig, axs =
plt.subplots(len(options), 1, constrained_layout=True) for ax, loc in
zip(axs, options): ax.plot(\[1, 2, 3\]) ax.set_xlabel(f\'xlabel
loc={loc!r}\', loc=loc)

options = \[\'bottom\', \'center\', \'top\'\] fig, axs = plt.subplots(1,
len(options), constrained_layout=True) for ax, loc in zip(axs, options):
ax.plot(\[1, 2, 3\]) ax.set_ylabel(f\'ylabel loc={loc!r}\', loc=loc)
:::

### Allow tick formatters to be set with str or function inputs

[\~.Axis.set_major_formatter]{.title-ref} and
[\~.Axis.set_minor_formatter]{.title-ref} now accept [str]{.title-ref}
or function inputs in addition to [\~.ticker.Formatter]{.title-ref}
instances. For a [str]{.title-ref} a
[\~.ticker.StrMethodFormatter]{.title-ref} is automatically generated
and used. For a function a [\~.ticker.FuncFormatter]{.title-ref} is
automatically generated and used. In other words, :

    ax.xaxis.set_major_formatter('{x} km')
    ax.xaxis.set_minor_formatter(lambda x, pos: str(x-5))

are shortcuts for:

    import matplotlib.ticker as mticker

    ax.xaxis.set_major_formatter(mticker.StrMethodFormatter('{x} km'))
    ax.xaxis.set_minor_formatter(
        mticker.FuncFormatter(lambda x, pos: str(x-5))

::: plot
from matplotlib import ticker

titles = \[\"\'{x} km\'\", \"lambda x, pos: str(x-5)\"\] formatters =
\[\'{x} km\', lambda x, pos: str(x-5)\]

fig, axs = plt.subplots(2, 1, figsize=(8, 2), constrained_layout=True)

for ax, title, formatter in zip(axs, titles, formatters):

:   \# only show the bottom spine
    ax.yaxis.set_major_locator(ticker.NullLocator()) for spine in
    \[\'top\', \'left\', \'right\'\]:
    ax.spines\[spine\].set_visible(False)

    \# define tick positions
    ax.xaxis.set_major_locator(ticker.MultipleLocator(1.00))
    ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.25))

    ax.tick_params(which=\'major\', width=1.00, length=5)
    ax.tick_params(which=\'minor\', width=0.75, length=2.5,
    labelsize=10) ax.set_xlim(0, 5) ax.set_ylim(0, 1) ax.text(0.0, 0.2,
    f\'ax.xaxis.set_major_formatter({title})\', transform=ax.transAxes,
    fontsize=14, fontname=\'Monospace\', color=\'tab:blue\')

    ax.xaxis.set_major_formatter(formatter)
:::

### `Axes.set_title` gains a *y* keyword argument to control auto positioning

[\~.axes.Axes.set_title]{.title-ref} tries to auto-position the title to
avoid any decorators on the top x-axis. This is not always desirable so
now *y* is an explicit keyword argument of
[\~.axes.Axes.set_title]{.title-ref}. It defaults to *None* which means
to use auto-positioning. If a value is supplied (i.e. the pre-3.0
default was `y=1.0`) then auto-positioning is turned off. This can also
be set with the new rcParameter `axes.titley`{.interpreted-text
role="rc"}.

::: plot
fig, axs = plt.subplots(1, 2, constrained_layout=True, figsize=(5, 2))
axs\[0\].set_title(\'y=0.7n\$\\[sum](){j_n} x_j\$\', y=0.7)
axs\[1\].set_title(\'y=Nonen\$\\[sum](){j_n} x_j\$\') plt.show()
:::

### Offset text is now set to the top when using `axis.tick_top()`

Solves the issue that the power indicator (e.g., 1e4) stayed on the
bottom, even if the ticks were on the top.

### Set zorder of contour labels

[\~.axes.Axes.clabel]{.title-ref} now accepts a *zorder* keyword
argument making it easier to set the *zorder* of contour labels. If not
specified, the default *zorder* of clabels used to always be 3 (i.e. the
default *zorder* of [\~.text.Text]{.title-ref}) irrespective of the
*zorder* passed to
[\~.axes.Axes.contour]{.title-ref}/[\~.axes.Axes.contourf]{.title-ref}.
The new default *zorder* for clabels has been changed to (`2 + zorder`
passed to [\~.axes.Axes.contour]{.title-ref} /
[\~.axes.Axes.contourf]{.title-ref}).

## Other changes

### New `Axes.axline` method

A new [\~.axes.Axes.axline]{.title-ref} method has been added to draw
infinitely long lines that pass through two points.

::: {.plot include-source="True"}
fig, ax = plt.subplots()

ax.axline((.1, .1), slope=5, color=\'C0\', label=\'by slope\')
ax.axline((.1, .2), (.8, .7), color=\'C3\', label=\'by points\')

ax.legend()
:::

### `imshow` now coerces 3D arrays with depth 1 to 2D

Starting from this version arrays of size MxNx1 will be coerced into MxN
for displaying. This means commands like
`plt.imshow(np.random.rand(3, 3, 1))` will no longer return an error
message that the image shape is invalid.

### Better control of `Axes.pie` normalization

Previously, [.Axes.pie]{.title-ref} would normalize its input *x* if
`sum(x) > 1`, but would do nothing if the sum were less than 1. This can
be confusing, so an explicit keyword argument *normalize* has been
added. By default, the old behavior is preserved.

By passing *normalize*, one can explicitly control whether any rescaling
takes place or whether partial pies should be created. If normalization
is disabled, and `sum(x) > 1`, then an error is raised.

::: plot

def label(x):

:   return \[str(v) for v in x\]

x = np.array(\[0.25, 0.3, 0.3\]) fig, ax = plt.subplots(2, 2,
constrained_layout=True)

ax\[0, 0\].pie(x, autopct=\'%1.1f%%\', labels=label(x), normalize=False)
ax\[0, 0\].set_title(\'normalize=False\') ax\[0, 1\].pie(x,
autopct=\'%1.2f%%\', labels=label(x), normalize=True) ax\[0,
1\].set_title(\'normalize=True\')

\# This is supposed to show the \'old\' behavior of not passing
*normalize* \# explicitly, but for the purposes of keeping the
documentation build \# warning-free, and future proof for when the
deprecation is made \# permanent, we pass *normalize* here explicitly
anyway. ax\[1, 0\].pie(x, autopct=\'%1.2f%%\', labels=label(x),
normalize=False) ax\[1, 0\].set_title(\'normalize unspecifiednsum(x) \<
1\') ax\[1, 1\].pie(x \* 10, autopct=\'%1.2f%%\', labels=label(x \* 10),
normalize=True) ax\[1, 1\].set_title(\'normalize unspecifiednsum(x) \>
1\')
:::

### Dates use a modern epoch

Matplotlib converts dates to days since an epoch using
[.dates.date2num]{.title-ref} (via [matplotlib.units]{.title-ref}).
Previously, an epoch of `0000-12-31T00:00:00` was used so that
`0001-01-01` was converted to 1.0. An epoch so distant in the past meant
that a modern date was not able to preserve microseconds because 2000
years times the 2\^(-52) resolution of a 64-bit float gives 14
microseconds.

Here we change the default epoch to the more reasonable UNIX default of
`1970-01-01T00:00:00` which for a modern date has 0.35 microsecond
resolution. (Finer resolution is not possible because we rely on
[datetime.datetime]{.title-ref} for the date locators). Access to the
epoch is provided by [\~.dates.get_epoch]{.title-ref}, and there is a
new `date.epoch`{.interpreted-text role="rc"} rcParam. The user may also
call [\~.dates.set_epoch]{.title-ref}, but it must be set *before* any
date conversion or plotting is used.

If you have data stored as ordinal floats in the old epoch, you can
convert them to the new ordinal using the following formula:

    new_ordinal = old_ordinal + mdates.date2num(np.datetime64('0000-12-31'))

### Lines now accept `MarkerStyle` instances as input

Similar to [\~.Axes.scatter]{.title-ref}, [\~.Axes.plot]{.title-ref} and
[\~.lines.Line2D]{.title-ref} now accept
[\~.markers.MarkerStyle]{.title-ref} instances as input for the *marker*
parameter:

    plt.plot(..., marker=matplotlib.markers.MarkerStyle("D"))

## Fonts

### Simple syntax to select fonts by absolute path

Fonts can now be selected by passing an absolute
[pathlib.Path]{.title-ref} to the *font* keyword argument of
[.Text]{.title-ref}.

### Improved font weight detection

Matplotlib is now better able to determine the weight of fonts from
their metadata, allowing to differentiate between fonts within the same
family more accurately.

## rcParams improvements

### `matplotlib.rc_context` can be used as a decorator

[matplotlib.rc_context]{.title-ref} can now be used as a decorator
(technically, it is now implemented as a
[contextlib.contextmanager]{.title-ref}), e.g., :

    @rc_context({"lines.linewidth": 2})
    def some_function(...):
        ...

### rcParams for controlling default \"raise window\" behavior

The new config option `figure.raise_window`{.interpreted-text role="rc"}
allows disabling of the raising of the plot window when calling
[\~.pyplot.show]{.title-ref} or [\~.pyplot.pause]{.title-ref}. The
`MacOSX` backend is currently not supported.

### Add generalized `mathtext.fallback` to rcParams

New `mathtext.fallback`{.interpreted-text role="rc"} rcParam. Takes
\"cm\", \"stix\", \"stixsans\" or \"none\" to turn fallback off. The
rcParam *mathtext.fallback_to_cm* is deprecated, but if used, will
override new fallback.

### Add `contour.linewidth` to rcParams

The new config option `contour.linewidth`{.interpreted-text role="rc"}
allows to control the default line width of contours as a float. When
set to `None`, the line widths fall back to
`lines.linewidth`{.interpreted-text role="rc"}. The config value is
overridden as usual by the *linewidths* argument passed to
[\~.axes.Axes.contour]{.title-ref} when it is not set to `None`.

## 3D Axes improvements

### `Axes3D` no longer distorts the 3D plot to match the 2D aspect ratio

Plots made with `~mpl_toolkits.mplot3d.axes3d.Axes3D`{.interpreted-text
role="class"} were previously stretched to fit a square bounding box. As
this stretching was done after the projection from 3D to 2D, it resulted
in distorted images if non-square bounding boxes were used. As of 3.3,
this no longer occurs.

Currently, modes of setting the aspect (via
[\~mpl_toolkits.mplot3d.axes3d.Axes3D.set_aspect]{.title-ref}) in data
space are not supported for Axes3D but may be in the future. If you want
to simulate having equal aspect in data space, set the ratio of your
data limits to match the value of [\~.get_box_aspect]{.title-ref}. To
control these ratios use the
[\~mpl_toolkits.mplot3d.axes3d.Axes3D.set_box_aspect]{.title-ref} method
which accepts the ratios as a 3-tuple of X:Y:Z. The default aspect ratio
is 4:4:3.

### 3D axes now support minor ticks

::: {.plot include-source="True"}
ax = plt.figure().add_subplot(projection=\'3d\')

ax.scatter(\[0, 1, 2\], \[1, 3, 5\], \[30, 50, 70\])

ax.set_xticks(\[0.25, 0.75, 1.25, 1.75\], minor=True)
ax.set_xticklabels(\[\'a\', \'b\', \'c\', \'d\'\], minor=True)

ax.set_yticks(\[1.5, 2.5, 3.5, 4.5\], minor=True)
ax.set_yticklabels(\[\'A\', \'B\', \'C\', \'D\'\], minor=True)

ax.set_zticks(\[35, 45, 55, 65\], minor=True)
ax.set_zticklabels(\[r\'\$alpha\$\', r\'\$beta\$\', r\'\$delta\$\',
r\'\$gamma\$\'\], minor=True)

ax.tick_params(which=\'major\', color=\'C0\', labelcolor=\'C0\',
width=5) ax.tick_params(which=\'minor\', color=\'C1\',
labelcolor=\'C1\', width=3)
:::

### Home/Forward/Backward buttons now work with 3D axes

## Interactive tool improvements

### More consistent toolbar behavior across backends

Toolbar features are now more consistent across backends. The history
buttons will auto-disable when there is no further action in a
direction. The pan and zoom buttons will be marked active when they are
in use.

In NbAgg and WebAgg, the toolbar buttons are now grouped similarly to
other backends. The WebAgg toolbar now uses the same icons as other
backends.

### Toolbar icons are now styled for dark themes

On dark themes, toolbar icons will now be inverted. When using the
GTK3Agg backend, toolbar icons are now symbolic, and both foreground and
background colors will follow the theme. Tooltips should also behave
correctly.

### Cursor text now uses a number of significant digits matching pointing precision

Previously, the x/y position displayed by the cursor text would usually
include far more significant digits than the mouse pointing precision
(typically one pixel). This is now fixed for linear scales.

### GTK / Qt zoom rectangle now black and white

This makes it visible even over a dark background.

### Event handler simplifications

The [.backend_bases.key_press_handler]{.title-ref} and
[.backend_bases.button_press_handler]{.title-ref} event handlers can now
be directly connected to a canvas with
`canvas.mpl_connect("key_press_event", key_press_handler)` and
`canvas.mpl_connect("button_press_event", button_press_handler)`, rather
than having to write wrapper functions that fill in the (now optional)
*canvas* and *toolbar* parameters.

## Functions to compute a Path\'s size

Various functions were added to [\~.bezier.BezierSegment]{.title-ref}
and [\~.path.Path]{.title-ref} to allow computation of the shape/size of
a [\~.path.Path]{.title-ref} and its composite Bezier curves.

In addition to the fixes below, [\~.bezier.BezierSegment]{.title-ref}
has gained more documentation and usability improvements, including
properties that contain its dimension, degree, control_points, and more.

### Better interface for Path segment iteration

[\~.path.Path.iter_bezier]{.title-ref} iterates through the
[\~.bezier.BezierSegment]{.title-ref}\'s that make up the Path. This is
much more useful typically than the existing
[\~.path.Path.iter_segments]{.title-ref} function, which returns the
absolute minimum amount of information possible to reconstruct the Path.

### Fixed bug that computed a Path\'s Bbox incorrectly

Historically, [\~.path.Path.get_extents]{.title-ref} has always simply
returned the Bbox of a curve\'s control points, instead of the Bbox of
the curve itself. While this is a correct upper bound for the path\'s
extents, it can differ dramatically from the Path\'s actual extents for
non-linear Bezier curves.

## Backend-specific improvements

### `savefig()` gained a *backend* keyword argument

The *backend* keyword argument to `savefig` can now be used to pick the
rendering backend without having to globally set the backend; e.g., one
can save PDFs using the pgf backend with
`savefig("file.pdf", backend="pgf")`.

### The SVG backend can now render hatches with transparency

The SVG backend now respects the hatch stroke alpha. Useful applications
are, among others, semi-transparent hatches as a subtle way to
differentiate columns in bar plots.

### SVG supports URLs on more artists

URLs on more artists (i.e., from [.Artist.set_url]{.title-ref}) will now
be saved in SVG files, namely, `Tick`s and `Line2D`s are now supported.

### Images in SVG will no longer be blurred in some viewers

A style is now supplied to images without interpolation
(`imshow(..., interpolation='none'`) so that SVG image viewers will no
longer perform interpolation when rendering themselves.

### Saving SVG now supports adding metadata

When saving SVG files, metadata can now be passed which will be saved in
the file using [Dublin
Core](https://www.dublincore.org/specifications/dublin-core/) and
[RDF](https://www.w3.org/1999/.status/PR-rdf-syntax-19990105/status). A
list of valid metadata can be found in the documentation for
[.FigureCanvasSVG.print_svg]{.title-ref}.

### Saving PDF metadata via PGF now consistent with PDF backend

When saving PDF files using the PGF backend, passed metadata will be
interpreted in the same way as with the PDF backend. Previously, this
metadata was only accepted by the PGF backend when saving a multi-page
PDF with [.backend_pgf.PdfPages]{.title-ref}, but is now allowed when
saving a single figure, as well.

### NbAgg and WebAgg no longer use jQuery & jQuery UI

Instead, they are implemented using vanilla JavaScript. Please report
any issues with browsers.