.. _whats-new: ========================== What's new in Matplotlib ========================== 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 .. For a release, add a new section after this, then comment out the include and toctree below by indenting them. Uncomment them after the release. .. include:: next_whats_new/README.rst .. toctree:: :glob: :maxdepth: 1 next_whats_new/* New in Matplotlib 2.2 ===================== Constrained Layout Manager -------------------------- .. warning:: Constrained Layout is **experimental**. The behaviour and API are subject to change, or the whole functionality may be removed without a deprecation period. A new method to automatically decide spacing between subplots and their organizing ``GridSpec`` instances has been added. It is meant to replace the venerable ``tight_layout`` method. It is invoked via a new ``constrained_layout=True`` kwarg to `~.figure.Figure` or `~.figure.subplots`. There are new ``rcParams`` for this package, and spacing can be more finely tuned with the new `~.set_constrained_layout_pads`. Features include: - Automatic spacing for subplots with a fixed-size padding in inches around subplots and all their decorators, and space between as a fraction of subplot size between subplots. - Spacing for `~.figure.suptitle`, and colorbars that are attached to more than one axes. - Nested `~.GridSpec` layouts using `~.GridSpecFromSubplotSpec`. For more details and capabilities please see the new tutorial: :doc:`/tutorials/intermediate/constrainedlayout_guide` Note the new API to access this: New ``plt.figure`` and ``plt.subplots`` kwarg: ``constrained_layout`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :meth:`~matplotlib.pyplot.figure` and :meth:`~matplotlib.pyplot.subplots` can now be called with ``constrained_layout=True`` kwarg to enable constrained_layout. New ``ax.set_position`` behaviour ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :meth:`~matplotlib.axes.set_position` now makes the specified axis no longer responsive to ``constrained_layout``, consistent with the idea that the user wants to place an axis manually. Internally, this means that old ``ax.set_position`` calls *inside* the library are changed to private ``ax._set_position`` calls so that ``constrained_layout`` will still work with these axes. New ``figure`` kwarg for ``GridSpec`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In order to facilitate ``constrained_layout``, ``GridSpec`` now accepts a ``figure`` keyword. This is backwards compatible, in that not supplying this will simply cause ``constrained_layout`` to not operate on the subplots orgainzed by this ``GridSpec`` instance. Routines that use ``GridSpec`` (e.g. ``fig.subplots``) have been modified to pass the figure to ``GridSpec``. xlabels and ylabels can now be automatically aligned ---------------------------------------------------- Subplot axes ``ylabels`` can be misaligned horizontally if the tick labels are very different widths. The same can happen to ``xlabels`` if the ticklabels are rotated on one subplot (for instance). The new methods on the `Figure` class: `Figure.align_xlabels` and `Figure.align_ylabels` will now align these labels horizontally or vertically. If the user only wants to align some axes, a list of axes can be passed. If no list is passed, the algorithm looks at all the labels on the figure. Only labels that have the same subplot locations are aligned. i.e. the ylabels are aligned only if the subplots are in the same column of the subplot layout. Alignemnt is persistent and automatic after these are called. A convenience wrapper `Figure.align_labels` calls both functions at once. .. plot:: import matplotlib.gridspec as gridspec fig = plt.figure(figsize=(5, 3), tight_layout=True) gs = gridspec.GridSpec(2, 2) ax = fig.add_subplot(gs[0,:]) ax.plot(np.arange(0, 1e6, 1000)) ax.set_ylabel('Test') for i in range(2): ax = fig.add_subplot(gs[1, i]) ax.set_ylabel('Booooo') ax.set_xlabel('Hello') if i == 0: for tick in ax.get_xticklabels(): tick.set_rotation(45) fig.align_labels() Axes legends now included in tight_bbox --------------------------------------- Legends created via ``ax.legend`` can sometimes overspill the limits of the axis. Tools like ``fig.tight_layout()`` and ``fig.savefig(bbox_inches='tight')`` would clip these legends. A change was made to include them in the ``tight`` calculations. Cividis colormap ---------------- A new dark blue/yellow colormap named 'cividis' was added. Like viridis, cividis is perceptually uniform and colorblind friendly. However, cividis also goes a step further: not only is it usable by colorblind users, it should actually look effectively identical to colorblind and non-colorblind users. For more details, see Nunez J, Anderton C, and Renslow R. (submitted). Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data." .. plot:: import matplotlib.pyplot as plt import numpy as np fig, ax = plt.subplots() pcm = ax.pcolormesh(np.random.rand(32,32), cmap='cividis') fig.colorbar(pcm) New style colorblind-friendly color cycle ----------------------------------------- A new style defining a color cycle has been added, tableau-colorblind10, to provide another option for colorblind-friendly plots. A demonstration of this new style can be found in the reference_ of style sheets. To load this color cycle in place of the default one:: import matplotlib.pyplot as plt plt.style.use('tableau-colorblind10') .. _reference: https://matplotlib.org/gallery/style_sheets/style_sheets_reference.html Support for numpy.datetime64 ---------------------------- Matplotlib has supported `datetime.datetime` dates for a long time in `matplotlib.dates`. We now support `numpy.datetime64` dates as well. Anywhere that `dateime.datetime` could be used, `numpy.datetime64` can be used. eg:: time = np.arange('2005-02-01', '2005-02-02', dtype='datetime64[h]') plt.plot(time) Writing animations with Pillow ------------------------------ It is now possible to use Pillow as an animation writer. Supported output formats are currently gif (Pillow>=3.4) and webp (Pillow>=5.0). Use e.g. as :: from __future__ import division from matplotlib import pyplot as plt from matplotlib.animation import FuncAnimation, PillowWriter fig, ax = plt.subplots() line, = plt.plot([0, 1]) def animate(i): line.set_ydata([0, i / 20]) return [line] anim = FuncAnimation(fig, animate, 20, blit=True) anim.save("movie.gif", writer=PillowWriter(fps=24)) plt.show() Slider UI widget can snap to discrete values -------------------------------------------- The slider UI widget can take the optional argument *valstep*. Doing so forces the slider to take on only discrete values, starting from *valmin* and counting up to *valmax* with steps of size *valstep*. If *closedmax==True*, then the slider will snap to *valmax* as well. ``capstyle`` and ``joinstyle`` attributes added to `Collection` --------------------------------------------------------------- The `Collection` class now has customizable ``capstyle`` and ``joinstyle`` attributes. This allows the user for example to set the ``capstyle`` of errorbars. *pad* kwarg added to ax.set_title --------------------------------- The method `axes.set_title` now has a *pad* kwarg, that specifies the distance from the top of an axes to where the title is drawn. The units of *pad* is points, and the default is the value of the (already-existing) ``rcParams['axes.titlepad']``. Comparison of 2 colors in Matplotlib ------------------------------------ As the colors in Matplotlib can be specified with a wide variety of ways, the `matplotlib.colors.same_color` method has been added which checks if two `~matplotlib.colors` are the same. Autoscaling a polar plot snaps to the origin -------------------------------------------- Setting the limits automatically in a polar plot now snaps the radial limit to zero if the automatic limit is nearby. This means plotting from zero doesn't automatically scale to include small negative values on the radial axis. The limits can still be set manually in the usual way using `set_ylim`. PathLike support ---------------- On Python 3.6+, `~matplotlib.pyplot.savefig`, `~matplotlib.pyplot.imsave`, `~matplotlib.pyplot.imread`, and animation writers now accept `os.PathLike`\s as input. `Axes.tick_params` can set gridline properties ---------------------------------------------- `Tick` objects hold gridlines as well as the tick mark and its label. `Axis.set_tick_params`, `Axes.tick_params` and `pyplot.tick_params` now have keyword arguments 'grid_color', 'grid_alpha', 'grid_linewidth', and 'grid_linestyle' for overriding the defaults in `rcParams`: 'grid.color', etc. `Axes.imshow` clips RGB values to the valid range ------------------------------------------------- When `Axes.imshow` is passed an RGB or RGBA value with out-of-range values, it now logs a warning and clips them to the valid range. The old behaviour, wrapping back in to the range, often hid outliers and made interpreting RGB images unreliable. Properties in `matplotlibrc` to place xaxis and yaxis tick labels ----------------------------------------------------------------- Introducing four new boolean properties in `.matplotlibrc` for default positions of xaxis and yaxis tick labels, namely, `xtick.labeltop`, `xtick.labelbottom`, `ytick.labelright` and `ytick.labelleft`. These can also be changed in rcParams. PGI bindings for gtk3 --------------------- The GTK3 backends can now use PGI_ instead of PyGObject_. PGI is a fairly incomplete binding for GObject, thus its use is not recommended; its main benefit is its availability on Travis (thus allowing CI testing for the gtk3agg and gtk3cairo backends). The binding selection rules are as follows: - if ``gi`` has already been imported, use it; else - if ``pgi`` has already been imported, use it; else - if ``gi`` can be imported, use it; else - if ``pgi`` can be imported, use it; else - error out. Thus, to force usage of PGI when both bindings are installed, import it first. .. _PGI: https://pgi.readthedocs.io/en/latest/ .. _PyGObject: http://pygobject.readthedocs.io/en/latest/# Cairo rendering for Qt, WX, and Tk canvases ------------------------------------------- The new ``Qt4Cairo``, ``Qt5Cairo``, ``WXCairo``, and ``TkCairo`` backends allow Qt, Wx, and Tk canvases to use Cairo rendering instead of Agg. Added support for QT in new ToolManager --------------------------------------- Now it is possible to use the ToolManager with Qt5 For example import matplotlib matplotlib.use('QT5AGG') matplotlib.rcParams['toolbar'] = 'toolmanager' import matplotlib.pyplot as plt plt.plot([1,2,3]) plt.show() Treat the new Tool classes experimental for now, the API will likely change and perhaps the rcParam as well The main example `examples/user_interfaces/toolmanager_sgskip.py` shows more details, just adjust the header to use QT instead of GTK3 TkAgg backend reworked to support PyPy -------------------------------------- PyPy_ can now plot using the TkAgg backend, supported on PyPy 5.9 and greater (both PyPy for python 2.7 and PyPy for python 3.5). .. _PyPy: https:/www.pypy.org Python logging library used for debug output -------------------------------------------- Matplotlib has in the past (sporadically) used an internal verbose-output reporter. This version converts those calls to using the standard python `logging` library. Support for the old ``rcParams`` ``verbose.level`` and ``verbose.fileo`` is dropped. The command-line options ``--verbose-helpful`` and ``--verbose-debug`` are still accepted, but deprecated. They are now equivalent to setting ``logging.INFO`` and ``logging.DEBUG``. The logger's root name is ``matplotlib`` and can be accessed from programs as:: import logging mlog = logging.getLogger('matplotlib') Instructions for basic usage are in :ref:`troubleshooting-faq` and for developers in :ref:`contributing`. .. _logging: https://docs.python.org/3/library/logging.html Improved `repr` for `Transform`\s --------------------------------- `Transform`\s now indent their `repr`\s in a more legible manner: .. code-block:: ipython In [1]: l, = plt.plot([]); l.get_transform() Out[1]: CompositeGenericTransform( TransformWrapper( BlendedAffine2D( IdentityTransform(), IdentityTransform())), CompositeGenericTransform( BboxTransformFrom( TransformedBbox( Bbox(x0=-0.05500000000000001, y0=-0.05500000000000001, x1=0.05500000000000001, y1=0.05500000000000001), TransformWrapper( BlendedAffine2D( IdentityTransform(), IdentityTransform())))), BboxTransformTo( TransformedBbox( Bbox(x0=0.125, y0=0.10999999999999999, x1=0.9, y1=0.88), BboxTransformTo( TransformedBbox( Bbox(x0=0.0, y0=0.0, x1=6.4, y1=4.8), Affine2D( [[ 100. 0. 0.] [ 0. 100. 0.] [ 0. 0. 1.]]))))))) Previous Whats New ================== .. toctree:: :glob: :maxdepth: 1 :reversed: prev_whats_new/changelog prev_whats_new/whats_new_*