.. _whats-new:
************************
What's new in matplotlib
************************
This page just covers the highlights -- for the full story, see the
`CHANGELOG `_
For a list of all of the issues and pull requests since the last
revision, see the :ref:`github-stats`.
.. note::
matplotlib 1.4 supports Python 2.6, 2.7, 3.3, and 3.4
matplotlib 1.3 supports Python 2.6, 2.7, 3.2, and 3.3
matplotlib 1.2 supports Python 2.6, 2.7, and 3.1
matplotlib 1.1 supports Python 2.4 to 2.7
.. contents:: Table of Contents
:depth: 3
.. _whats-new-1-4:
new in matplotlib-1.4
=====================
Thomas A. Caswell served as the release manager for the 1.4 release.
New colormap
------------
In heatmaps, a green-to-red spectrum is often used to indicate intensity of
activity, but this can be problematic for the red/green colorblind. A new,
colorblind-friendly colormap is now available at :class:`matplotlib.cm.Wistia`.
This colormap maintains the red/green symbolism while achieving deuteranopic
legibility through brightness variations. See
`here `_
for more information.
The nbagg backend
-----------------
Phil Elson added a new backend, named "nbagg", which enables interactive
figures in a live IPython notebook session. The backend makes use of the
infrastructure developed for the webagg backend, which itself gives
standalone server backed interactive figures in the browser, however nbagg
does not require a dedicated matplotlib server as all communications are
handled through the IPython Comm machinery.
As with other backends nbagg can be enabled inside the IPython notebook with::
import matplotlib
matplotlib.use('nbagg')
Once figures are created and then subsequently shown, they will placed in an
interactive widget inside the notebook allowing panning and zooming in the
same way as any other matplotlib backend. Because figures require a connection
to the IPython notebook server for their interactivity, once the notebook is
saved, each figure will be rendered as a static image - thus allowing
non-interactive viewing of figures on services such as
`nbviewer `_.
New plotting features
---------------------
Power-law normalization
```````````````````````
Ben Gamari added a power-law normalization method,
:class:`~matplotlib.colors.PowerNorm`. This class maps a range of
values to the interval [0,1] with power-law scaling with the exponent
provided by the constructor's `gamma` argument. Power law normalization
can be useful for, e.g., emphasizing small populations in a histogram.
Fully customizable boxplots
```````````````````````````
Paul Hobson overhauled the :func:`~matplotlib.pyplot.boxplot` method such
that it is now completely customizable in terms of the styles and positions
of the individual artists. Under the hood, :func:`~matplotlib.pyplot.boxplot`
relies on a new function (:func:`~matplotlib.cbook.boxplot_stats`), which
accepts any data structure currently compatible with
:func:`~matplotlib.pyplot.boxplot`, and returns a list of dictionaries
containing the positions for each element of the boxplots. Then
a second method, :func:`~matplotlib.Axes.bxp` is called to draw the boxplots
based on the stats.
The :func:`~matplotlib.pyplot.boxplot` function can be used as before to
generate boxplots from data in one step. But now the user has the
flexibility to generate the statistics independently, or to modify the
output of :func:`~matplotlib.cbook.boxplot_stats` prior to plotting
with :func:`~matplotlib.Axes.bxp`.
Lastly, each artist (e.g., the box, outliers, cap, notches) can now be
toggled on or off and their styles can be passed in through individual
kwargs. See the examples:
:ref:`statistics-boxplot_demo` and
:ref:`statistics-bxp_demo`
Added a bool kwarg, :code:`manage_xticks`, which if False disables the management
of the ticks and limits on the x-axis by :func:`~matplotlib.axes.Axes.bxp`.
Support for datetime axes in 2d plots
`````````````````````````````````````
Andrew Dawson added support for datetime axes to
:func:`~matplotlib.pyplot.contour`, :func:`~matplotlib.pyplot.contourf`,
:func:`~matplotlib.pyplot.pcolormesh` and :func:`~matplotlib.pyplot.pcolor`.
Support for additional spectrum types
`````````````````````````````````````
Todd Jennings added support for new types of frequency spectrum plots:
:func:`~matplotlib.pyplot.magnitude_spectrum`,
:func:`~matplotlib.pyplot.phase_spectrum`, and
:func:`~matplotlib.pyplot.angle_spectrum`, as well as corresponding functions
in mlab.
He also added these spectrum types to :func:`~matplotlib.pyplot.specgram`,
as well as adding support for linear scaling there (in addition to the
existing dB scaling). Support for additional spectrum types was also added to
:func:`~matplotlib.mlab.specgram`.
He also increased the performance for all of these functions and plot types.
Support for detrending and windowing 2D arrays in mlab
``````````````````````````````````````````````````````
Todd Jennings added support for 2D arrays in the
:func:`~matplotlib.mlab.detrend_mean`, :func:`~matplotlib.mlab.detrend_none`,
and :func:`~matplotlib.mlab.detrend`, as well as adding
:func:`~matplotlib.mlab.apply_window` which support windowing 2D arrays.
Support for strides in mlab
```````````````````````````
Todd Jennings added some functions to mlab to make it easier to use numpy
strides to create memory-efficient 2D arrays. This includes
:func:`~matplotlib.mlab.stride_repeat`, which repeats an array to create a 2D
array, and :func:`~matplotlib.mlab.stride_windows`, which uses a moving window
to create a 2D array from a 1D array.
Formatter for new-style formatting strings
``````````````````````````````````````````
Added `FormatStrFormatterNewStyle` which does the same job as
`FormatStrFormatter`, but accepts new-style formatting strings
instead of printf-style formatting strings
Consistent grid sizes in streamplots
````````````````````````````````````
:func:`~matplotlib.pyplot.streamplot` uses a base grid size of 30x30 for both
`density=1` and `density=(1, 1)`. Previously a grid size of 30x30 was used for
`density=1`, but a grid size of 25x25 was used for `density=(1, 1)`.
Get a list of all tick labels (major and minor)
```````````````````````````````````````````````
Added the `kwarg` 'which' to :func:`~matplotlib.Axes.get_xticklabels`,
:func:`~matplotlib.Axes.get_yticklabels` and
:func:`~matplotlib.Axis.get_ticklabels`. 'which' can be 'major', 'minor', or
'both' select which ticks to return, like
:func:`~matplotlib.Axis.set_ticks_position`. If 'which' is `None` then the old
behaviour (controlled by the bool `minor`).
Separate horizontal/vertical axes padding support in ImageGrid
``````````````````````````````````````````````````````````````
The `kwarg` 'axes_pad' to :class:`mpl_toolkits.axes_grid1.ImageGrid` can now
be a tuple if separate horizontal/vertical padding is needed.
This is supposed to be very helpful when you have a labelled legend next to
every subplot and you need to make some space for legend's labels.
Support for skewed transformations
``````````````````````````````````
The :class:`~matplotlib.transforms.Affine2D` gained additional methods
`skew` and `skew_deg` to create skewed transformations. Additionally,
matplotlib internals were cleaned up to support using such transforms in
:class:`~matplotlib.Axes`. This transform is important for some plot types,
specifically the Skew-T used in meteorology.
.. plot:: mpl_examples/api/skewt.py
Support for specifying properties of wedge and text in pie charts.
``````````````````````````````````````````````````````````````````
Added the `kwargs` 'wedgeprops' and 'textprops' to :func:`~matplotlib.Axes.pie`
to accept properties for wedge and text objects in a pie. For example, one can
specify wedgeprops = {'linewidth':3} to specify the width of the borders of
the wedges in the pie. For more properties that the user can specify, look at
the docs for the wedge and text objects.
Fixed the direction of errorbar upper/lower limits
``````````````````````````````````````````````````
Larry Bradley fixed the :func:`~matplotlib.pyplot.errorbar` method such
that the upper and lower limits (*lolims*, *uplims*, *xlolims*,
*xuplims*) now point in the correct direction.
More consistent add-object API for Axes
```````````````````````````````````````
Added the Axes method :meth:`~matplotlib.axes.Axes.add_image` to put image
handling on a par with artists, collections, containers, lines, patches,
and tables.
Violin Plots
````````````
Per Parker, Gregory Kelsie, Adam Ortiz, Kevin Chan, Geoffrey Lee, Deokjae
Donald Seo, and Taesu Terry Lim added a basic implementation for violin
plots. Violin plots can be used to represent the distribution of sample data.
They are similar to box plots, but use a kernel density estimation function to
present a smooth approximation of the data sample used. The added features are:
:func:`~matplotlib.Axes.violin` - Renders a violin plot from a collection of
statistics.
:func:`~matplotlib.cbook.violin_stats` - Produces a collection of statistics
suitable for rendering a violin plot.
:func:`~matplotlib.pyplot.violinplot` - Creates a violin plot from a set of
sample data. This method makes use of :func:`~matplotlib.cbook.violin_stats`
to process the input data, and :func:`~matplotlib.cbook.violin_stats` to
do the actual rendering. Users are also free to modify or replace the output of
:func:`~matplotlib.cbook.violin_stats` in order to customize the violin plots
to their liking.
This feature was implemented for a software engineering course at the
University of Toronto, Scarborough, run in Winter 2014 by Anya Tafliovich.
More `markevery` options to show only a subset of markers
`````````````````````````````````````````````````````````
Rohan Walker extended the `markevery` property in
:class:`~matplotlib.lines.Line2D`. You can now specify a subset of markers to
show with an int, slice object, numpy fancy indexing, or float. Using a float
shows markers at approximately equal display-coordinate-distances along the
line.
Added size related functions to specialized `Collections`
`````````````````````````````````````````````````````````
Added the `get_size` and `set_size` functions to control the size of
elements of specialized collections (
:class:`~matplotlib.collections.AsteriskPolygonCollection`
:class:`~matplotlib.collections.BrokenBarHCollection`
:class:`~matplotlib.collections.CircleCollection`
:class:`~matplotlib.collections.PathCollection`
:class:`~matplotlib.collections.PolyCollection`
:class:`~matplotlib.collections.RegularPolyCollection`
:class:`~matplotlib.collections.StarPolygonCollection`).
Fixed the mouse coordinates giving the wrong theta value in Polar graph
```````````````````````````````````````````````````````````````````````
Added code to
:func:`~matplotlib.InvertedPolarTransform.transform_non_affine`
to ensure that the calculated theta value was between the range of 0 and 2 * pi
since the problem was that the value can become negative after applying the
direction and rotation to the theta calculation.
Simple quiver plot for mplot3d toolkit
``````````````````````````````````````
A team of students in an *Engineering Large Software Systems* course, taught
by Prof. Anya Tafliovich at the University of Toronto, implemented a simple
version of a quiver plot in 3D space for the mplot3d toolkit as one of their
term project. This feature is documented in :func:`~mpl_toolkits.mplot3d.Axes3D.quiver`.
The team members are: Ryan Steve D'Souza, Victor B, xbtsw, Yang Wang, David,
Caradec Bisesar and Vlad Vassilovski.
.. plot:: mpl_examples/mplot3d/quiver3d_demo.py
polar-plot r-tick locations
```````````````````````````
Added the ability to control the angular position of the r-tick labels
on a polar plot via :func:`~matplotlib.Axes.axes.set_rlabel_position`.
Date handling
-------------
n-d array support for date conversion
``````````````````````````````````````
Andrew Dawson added support for n-d array handling to
:func:`matplotlib.dates.num2date`, :func:`matplotlib.dates.date2num`
and :func:`matplotlib.dates.datestr2num`. Support is also added to the unit
conversion interfaces :class:`matplotlib.dates.DateConverter` and
:class:`matplotlib.units.Registry`.
Configuration (rcParams)
------------------------
``savefig.transparent`` added
`````````````````````````````
Controls whether figures are saved with a transparent
background by default. Previously `savefig` always defaulted
to a non-transparent background.
``axes.titleweight``
````````````````````
Added rcParam to control the weight of the title
``axes.formatter.useoffset`` added
``````````````````````````````````
Controls the default value of `useOffset` in `ScalarFormatter`. If
`True` and the data range is much smaller than the data average, then
an offset will be determined such that the tick labels are
meaningful. If `False` then the full number will be formatted in all
conditions.
``nbagg.transparent`` added
`````````````````````````````
Controls whether nbagg figures have a transparent
background. ``nbagg.transparent`` is ``True`` by default.
XDG compliance
``````````````
Matplotlib now looks for configuration files (both rcparams and style) in XDG
compliant locations.
``style`` package added
-----------------------
You can now easily switch between different styles using the new ``style``
package::
>>> from matplotlib import style
>>> style.use('dark_background')
Subsequent plots will use updated colors, sizes, etc. To list all available
styles, use::
>>> print style.available
You can add your own custom ``