What's new in Matplotlib 3.1

For a list of all of the issues and pull requests since the last

revision, see the :ref:`github-stats`.

.. contents:: Table of Contents

:depth: 4

.. toctree::

:maxdepth: 4

New Features

`~.dates.ConciseDateFormatter`

The automatic date formatter used by default can be quite verbose. A new

formatter can be accessed that tries to make the tick labels appropriately

concise.

.. plot::

import datetime

import matplotlib.pyplot as plt

import matplotlib.dates as mdates

import numpy as np

# make a timeseries...

base = datetime.datetime(2005, 2, 1)

dates = np.array([base + datetime.timedelta(hours= 2 * i)

for i in range(732)])

N = len(dates)

np.random.seed(19680801)

y = np.cumsum(np.random.randn(N))

lims = [(np.datetime64('2005-02'), np.datetime64('2005-04')),

(np.datetime64('2005-02-03'), np.datetime64('2005-02-15')),

(np.datetime64('2005-02-03 11:00'), np.datetime64('2005-02-04 13:20'))]

fig, axs = plt.subplots(3, 1, constrained_layout=True)

for nn, ax in enumerate(axs):

# activate the formatter here.

locator = mdates.AutoDateLocator()

formatter = mdates.ConciseDateFormatter(locator)

ax.xaxis.set_major_locator(locator)

ax.xaxis.set_major_formatter(formatter)

ax.plot(dates, y)

ax.set_xlim(lims[nn])

axs[0].set_title('Concise Date Formatter')

plt.show()

Secondary x/y Axis support

A new method provides the ability to add a second axis to an existing

axes via `.Axes.secondary_xaxis` and `.Axes.secondary_yaxis`. See

:doc:`/gallery/subplots_axes_and_figures/secondary_axis` for examples.

.. plot::

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(5, 3))

ax.plot(range(360))

ax.secondary_xaxis('top', functions=(np.deg2rad, np.rad2deg))

`~.scale.FuncScale` for arbitrary axes scales

A new `~.scale.FuncScale` class was added (and `~.scale.FuncTransform`)

to allow the user to have arbitrary scale transformations without having to

write a new subclass of `~.scale.ScaleBase`. This can be accessed by::

where ``forward`` and ``inverse`` are callables that return the scale

transform and its inverse. See the last example in

:doc:`/gallery/scales/scales`.

Legend for scatter

A new method for creating legends for scatter plots has been

introduced. Previously, in order to obtain a legend for a

:meth:`~.axes.Axes.scatter` plot, one could either plot several

scatters, each with an individual label, or create proxy artists to

show in the legend manually. Now,

:class:`~.collections.PathCollection` provides a method

:meth:`~.collections.PathCollection.legend_elements` to obtain the

handles and labels for a scatter plot in an automated way. This makes

creating a legend for a scatter plot as easy as

An example can be found in :ref:`automatedlegendcreation`.

Matplotlib no longer requires framework app build on MacOSX backend

Previous versions of matplotlin required a Framework build of python to

work. The app type was updated to no longer require this, so the MacOSX

backend should work with non-framework python.

This also adds support for the MacOSX backend for PyPy3.

Figure, FigureCanvas, and Backends

Figure.frameon is now a direct proxy for the Figure patch visibility state

Accessing ``Figure.frameon`` (including via ``get_frameon`` and ``set_frameon``

now directly forwards to the visibility of the underlying Rectangle artist

(``Figure.patch.get_frameon``, ``Figure.patch.set_frameon``).

*pil_kwargs* argument added to savefig

Matplotlib uses Pillow to handle saving to the JPEG and TIFF formats. The

`~.Figure.savefig()` function gained a *pil_kwargs* keyword argument, which can

be used to forward arguments to Pillow's `pillow.Image.save()`.

The *pil_kwargs* argument can also be used when saving to PNG. In that case,

Matplotlib also uses Pillow's `pillow.Image.save()` instead of going through its

own builtin PNG support.

Add ``inaxes`` method to `.FigureCanvasBase`

The `.FigureCanvasBase` class has now an `~.FigureCanvasBase.inaxes`

method to check whether a point is in an axes and returns the topmost

axes, else None.

cairo backend defaults to pycairo instead of cairocffi

This leads to faster import/runtime performance in some cases. The backend

will fall back to cairocffi in case pycairo isn't available.

Axes and Artists

axes_grid1 and axisartist Axes no longer draw spines twice

Previously, spines of `.axes_grid1` and `.axisartist` Axes would be drawn twice,

leading to a "bold" appearance. This is no longer the case.

Return type of ArtistInspector.get_aliases changed

`.ArtistInspector.get_aliases` previously returned the set of aliases as

``{fullname: {alias1: None, alias2: None, ...}}``. The dict-to-None mapping

was used to simulate a set in earlier versions of Python. It has now been

replaced by a set, i.e. ``{fullname: {alias1, alias2, ...}}``.

This value is also stored in `.ArtistInspector.aliasd`, which has likewise

changed.

`.ConnectionPatch` accepts arbitrary transforms

Alternatively to strings like ``"data"`` or ``"axes fraction"``

`ConnectionPatch` now accepts any `~matplotlib.transforms.Transform`

as input for the ``coordsA`` and ``coordsB`` argument. This allows to

draw lines between points defined in different user defined coordinate

systems. Also see the :doc:`Connect Simple01 example

</gallery/userdemo/connect_simple01>`.

mplot3d Line3D now allows {set,get}_data_3d

Lines created with the 3d projection in mplot3d can now access the

data using `~.mplot3d.art3d.Line3D.get_data_3d()` which returns a

tuple of array_likes containing the (x, y, z) data. The equivalent

`~.mplot3d.art3d.Line3D.set_data_3d` can be used to modify the data of

an existing Line3D.

``Axes3D.voxels`` now shades the resulting voxels

The :meth:`~mpl_toolkits.mplot3d.Axes3D.voxels` method now takes a

*shade* parameter that defaults to `True`. This shades faces based

on their orientation, behaving just like the matching parameters to

:meth:`~mpl_toolkits.mplot3d.Axes3D.trisurf` and

:meth:`~mpl_toolkits.mplot3d.Axes3D.bar3d`. The plot below shows how

this affects the output.

.. plot::

import matplotlib.pyplot as plt

import numpy as np

# prepare some coordinates

x, y, z = np.indices((8, 8, 8))

# draw cuboids in the top left and bottom right corners, and a link between them

cube1 = (x < 3) & (y < 3) & (z < 3)

cube2 = (x >= 5) & (y >= 5) & (z >= 5)

link = abs(x - y) + abs(y - z) + abs(z - x) <= 2

# combine the objects into a single boolean array

voxels = cube1 | cube2 | link

# set the colors of each object

colors = np.empty(voxels.shape, dtype=object)

colors[link] = 'red'

colors[cube1] = 'blue'

colors[cube2] = 'green'

# and plot everything

fig = plt.figure(figsize=plt.figaspect(0.5))

ax, ax_shaded = fig.subplots(1, 2, subplot_kw=dict(projection='3d'))

ax.voxels(voxels, facecolors=colors, edgecolor='k', shade=False)

ax.set_title("Unshaded")

ax_shaded.voxels(voxels, facecolors=colors, edgecolor='k', shade=True)

ax_shaded.set_title("Shaded (default)")

plt.show()

Axis and Ticks

Added `.Axis.get_inverted` and `.Axis.set_inverted`

The `.Axis.get_inverted` and `.Axis.set_inverted` methods query and set whether

the axis uses "inverted" orientation (i.e. increasing to the left for the

x-axis and to the bottom for the y-axis).

They perform tasks similar to `.Axes.xaxis_inverted`,

`.Axes.yaxis_inverted`, `.Axes.invert_xaxis`, and

`.Axes.invert_yaxis`, with the specific difference that

`.Axes..set_inverted` makes it easier to set the invertedness of an

axis regardless of whether it had previously been inverted before.

Adjust default minor tick spacing

Default minor tick spacing was changed from 0.625 to 0.5 for major ticks spaced

2.5 units apart.

`.EngFormatter` now accepts `usetex`, `useMathText` as keyword only arguments

A public API has been added to `EngFormatter` to control how the

numbers in the ticklabels will be rendered. By default,

``useMathText`` evaluates to

:rc:`axes.formatter.use_mathtext'` and ``usetex`` evaluates

to :rc:`'text.usetex'`.

If either is `True` then the numbers will be encapsulated by ``$``

signs. When using ``TeX`` this implies that the numbers will be shown

in TeX's math font. When using mathtext, the ``$`` signs around

numbers will ensure unicode rendering (as implied by mathtext). This

will make sure that the minus signs in the ticks are rendered as the

unicode=minus (U+2212) when using mathtext (without relying on the

`~.Fomatter.fix_minus` method).

Animation and Interactivity

Support for forward/backward mouse buttons

Figure managers now support a ``button_press`` event for mouse

buttons, similar to the ``key_press`` events. This allows binding

actions to mouse buttons (see `.MouseButton`) The first application of

this mechanism is support of forward/backward mouse buttons in figures

created with the Qt5 backend.

*progress_callback* argument to `~.Animation.save()`

The method `.Animation.save` gained an optional

*progress_callback* argument to notify the saving progress.

Add ``cache_frame_data`` keyword-only argument into `.animation.FuncAnimation`

`.matplotlib.animation.FuncAnimation` has been caching frame data by

default; however, this caching is not ideal in certain cases e.g. When

`.FuncAnimation` needs to be only drawn(not saved) interactively and

memory required by frame data is quite large. By adding

*cache_frame_data* keyword-only argument, users can now disable this

caching; thereby, this new argument provides a fix for issue

:ghissue:`8528`.

Endless Looping GIFs with PillowWriter

We acknowledge that most people want to watch a gif more than

once. Saving an animation as a gif with PillowWriter now produces an

endless looping gif.

Adjusted `.matplotlib.widgets.Slider` to have vertical orientation

The :class:`matplotlib.widgets.Slider` widget now takes an optional

argument *orientation* which indicates the direction

(``'horizontal'`` or ``'vertical'``) that the slider should take.

Improved formatting of image values under cursor when a colorbar is present

When a colorbar is present, its formatter is now used to format the image

values under the mouse cursor in the status bar. For example, for an image

displaying the values 10,000 and 10,001, the statusbar will now (using default

settings) display the values as ``10000`` and ``10001``), whereas both values

were previously displayed as ``1e+04``.

MouseEvent button attribute is now an IntEnum

The :attr:`button` attribute of `~.MouseEvent` instances can take the values

None, 1 (left button), 2 (middle button), 3 (right button), "up" (scroll), and

"down" (scroll). For better legibility, the 1, 2, and 3 values are now

represented using the `IntEnum` class `matplotlib.backend_bases.MouseButton`,

with the values `MouseButton.LEFT` (``== 1``), `MouseButton.MIDDLE` (``== 2``),

and `MouseButton.RIGHT` (``== 3``).

Configuration, Install, and Development

The MATPLOTLIBRC environment variable can now point to any "file" path

This includes device files; in particular, on Unix systems, one can set

``MATPLOTLIBRC`` to ``/dev/null`` to ignore the user's matplotlibrc file and

fall back to Matplotlib's defaults.

As a reminder, if ``MATPLOTLIBRC`` points to a directory, Matplotlib will try

to load the matplotlibrc file from ``$MATPLOTLIBRC/matplotlibrc``.

Allow LaTeX code ``pgf.preamble`` and ``text.latex.preamble`` in MATPLOTLIBRC file

Previously, the rc file keys :rc:`pgf.preamble` and

:rc:`text.latex.preamble` were parsed using commas as separators. This

would break valid LaTeX code, such as::

The parsing has been modified to pass the complete line to the LaTeX

system, keeping all commas. Passing a list of strings from within a

Python script still works as it used to.

New logging API

`matplotlib.set_loglevel` / `.pyplot.set_loglevel` can be called to

display more (or less) detailed logging output.

Previous What's New

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