API Changes in 2.0.0

Deprecation and removal

Color of Axes

The axisbg and axis_bgcolor properties on Axes have been deprecated in favor of facecolor.

GTK and GDK backends deprecated

The GDK and GTK backends have been deprecated. These obsolete backends allow figures to be rendered via the GDK API to files and GTK2 figures. They are untested and known to be broken, and their use has been discouraged for some time. Instead, use the GTKAgg and GTKCairo backends for rendering to GTK2 windows.

WX backend deprecated

The WX backend has been deprecated. It is untested, and its use has been discouraged for some time. Instead, use the WXAgg backend for rendering figures to WX windows.

CocoaAgg backend removed

The deprecated and not fully functional CocoaAgg backend has been removed.

round removed from TkAgg Backend

The TkAgg backend had its own implementation of the round function. This was unused internally and has been removed. Instead, use either the round builtin function or numpy.around.

'hold' functionality deprecated

The 'hold' keyword argument and all functions and methods related to it are deprecated, along with the axes.hold rcParams entry. The behavior will remain consistent with the default hold=True state that has long been in place. Instead of using a function or keyword argument (hold=False) to change that behavior, explicitly clear the axes or figure as needed prior to subsequent plotting commands.

Artist.update has return value

The methods matplotlib.artist.Artist.set, matplotlib.artist.Artist.update, and the function matplotlib.artist.setp now use a common codepath to look up how to update the given artist properties (either using the setter methods or an attribute/property).

The behavior of matplotlib.artist.Artist.update is slightly changed to return a list of the values returned from the setter methods to avoid changing the API of matplotlib.artist.Artist.set and matplotlib.artist.setp.

The keys passed into matplotlib.artist.Artist.update are now converted to lower case before being processed, to match the behavior of matplotlib.artist.Artist.set and matplotlib.artist.setp. This should not break any user code because there are no set methods with capitals in their names, but this puts a constraint on naming properties in the future.

Legend initializers gain edgecolor and facecolor keyword arguments

The Legend background patch (or 'frame') can have its edgecolor and facecolor determined by the corresponding keyword arguments to the matplotlib.legend.Legend initializer, or to any of the methods or functions that call that initializer. If left to their default values of None, their values will be taken from matplotlib.rcParams. The previously-existing framealpha kwarg still controls the alpha transparency of the patch.

Qualitative colormaps

Colorbrewer's qualitative/discrete colormaps ("Accent", "Dark2", "Paired", "Pastel1", "Pastel2", "Set1", "Set2", "Set3") are now implemented as ListedColormap instead of LinearSegmentedColormap.

To use these for images where categories are specified as integers, for instance, use:

plt.imshow(x, cmap='Dark2', norm=colors.NoNorm())

Change in the draw_image backend API

The draw_image method implemented by backends has changed its interface.

This change is only relevant if the backend declares that it is able to transform images by returning True from option_scale_image. See the draw_image docstring for more information.

matplotlib.ticker.LinearLocator algorithm update

The matplotlib.ticker.LinearLocator is used to define the range and location of axis ticks when the user wants an exact number of ticks. LinearLocator thus differs from the default locator MaxNLocator, for which the user specifies a maximum number of intervals rather than a precise number of ticks.

The view range algorithm in matplotlib.ticker.LinearLocator has been changed so that more convenient tick locations are chosen. The new algorithm returns a plot view range that is a multiple of the user-requested number of ticks. This ensures tick marks will be located at whole integers more consistently. For example, when both y-axes of a``twinx`` plot use matplotlib.ticker.LinearLocator with the same number of ticks, their y-tick locations and grid lines will coincide.

matplotlib.ticker.LogLocator gains numticks kwarg

The maximum number of ticks generated by the LogLocator can now be controlled explicitly via setting the new 'numticks' kwarg to an integer. By default the kwarg is None which internally sets it to the 'auto' string, triggering a new algorithm for adjusting the maximum according to the axis length relative to the ticklabel font size.

matplotlib.ticker.LogFormatter: two new kwargs

Previously, minor ticks on log-scaled axes were not labeled by default. An algorithm has been added to the LogFormatter to control the labeling of ticks between integer powers of the base. The algorithm uses two parameters supplied in a kwarg tuple named 'minor_thresholds'. See the docstring for further explanation.

To improve support for axes using SymmetricalLogLocator, a linthresh keyword argument was added.

New defaults for 3D quiver function in mpl_toolkits.mplot3d.axes3d.py

Matplotlib has both a 2D and a 3D quiver function. These changes affect only the 3D function and make the default behavior of the 3D function match the 2D version. There are two changes:

1. The 3D quiver function previously normalized the arrows to be the same length, which makes it unusable for situations where the arrows should be different lengths and does not match the behavior of the 2D function. This normalization behavior is now controlled with the normalize keyword, which defaults to False.
2. The pivot keyword now defaults to tail instead of tip. This was done in order to match the default behavior of the 2D quiver function.

To obtain the previous behavior with the 3D quiver function, one can call the function with

ax.quiver(x, y, z, u, v, w, normalize=True, pivot='tip')

where "ax" is an Axes3d object created with something like

import mpl_toolkits.mplot3d.axes3d
ax = plt.subplot(111, projection='3d')

Stale figure behavior

Attempting to draw the figure will now mark it as not stale (independent if the draw succeeds). This change is to prevent repeatedly trying to re-draw a figure which is raising an error on draw. The previous behavior would only mark a figure as not stale after a full re-draw succeeded.

The spectral colormap is now nipy_spectral

The colormaps formerly known as spectral and spectral_r have been replaced by nipy_spectral and nipy_spectral_r since Matplotlib 1.3.0. Even though the colormap was deprecated in Matplotlib 1.3.0, it never raised a warning. As of Matplotlib 2.0.0, using the old names raises a deprecation warning. In the future, using the old names will raise an error.

Default install no longer includes test images

To reduce the size of wheels and source installs, the tests and baseline images are no longer included by default.

To restore installing the tests and images, use a setup.cfg with

[packages]
tests = True
toolkits_tests = True

in the source directory at build/install time.