.. _whats-new-1-5:

New in matplotlib 1.5
=====================

.. contents:: Table of Contents
   :depth: 2


.. note::

   matplotlib 1.5 supports Python 2.7, 3.4, and 3.5






Interactive OO usage
--------------------

All `Artists` now keep track of if their internal state has been
changed but not reflected in the display ('stale') by a call to
``draw``.  It is thus possible to pragmatically determine if a given
`Figure` needs to be re-drawn in an interactive session.

To facilitate interactive usage a ``draw_all`` method has been added
to ``pyplot`` which will redraw all of the figures which are 'stale'.

To make this convenient for interactive use matplotlib now registers
a function either with IPython's 'post_execute' event or with the
displayhook in the standard python REPL to automatically call
``plt.draw_all`` just before control is returned to the REPL.  This ensures
that the draw command is deferred and only called once.

The upshot of this is that for interactive backends (including
``%matplotlib notebook``) in interactive mode (with ``plt.ion()``)

.. ipython :: python

   import matplotlib.pyplot as plt

   fig, ax = plt.subplots()

   ln, = ax.plot([0, 1, 4, 9, 16])

   plt.show()

   ln.set_color('g')


will automatically update the plot to be green.  Any subsequent
modifications to the ``Artist`` objects will do likewise.

This is the first step of a larger consolidation and simplification of
the pyplot internals.


Working with labeled data like pandas DataFrames
------------------------------------------------
Plot methods which take arrays as inputs can now also work with labeled data
and unpack such data.

This means that the following two examples produce the same plot:

Example ::

    df = pandas.DataFrame({"var1":[1,2,3,4,5,6], "var2":[1,2,3,4,5,6]})
    plt.plot(df["var1"], df["var2"])


Example ::

    plt.plot("var1", "var2", data=df)

This works for most plotting methods, which expect arrays/sequences as
inputs.  ``data`` can be anything which supports ``__getitem__``
(``dict``, ``pandas.DataFrame``, ``h5py``, ...) to access ``array`` like
values with string keys.

In addition to this, some other changes were made, which makes working with
labeled data (ex ``pandas.Series``) easier:

* For plotting methods with ``label`` keyword argument, one of the
  data inputs is designated as the label source.  If the user does not
  supply a ``label`` that value object will be introspected for a
  label, currently by looking for a ``name`` attribute.  If the value
  object does not have a ``name`` attribute but was specified by as a
  key into the ``data`` kwarg, then the key is used.  In the above
  examples, this results in an implicit ``label="var2"`` for both
  cases.

* ``plot()`` now uses the index of a ``Series`` instead of
  ``np.arange(len(y))``, if no ``x`` argument is supplied.


Added ``axes.prop_cycle`` key to rcParams
-----------------------------------------

This is a more generic form of the now-deprecated ``axes.color_cycle`` param.
Now, we can cycle more than just colors, but also linestyles, hatches,
and just about any other artist property. Cycler notation is used for
defining property cycles. Adding cyclers together will be like you are
`zip()`-ing together two or more property cycles together::

    axes.prop_cycle: cycler('color', 'rgb') + cycler('lw', [1, 2, 3])

You can even multiply cyclers, which is like using `itertools.product()`
on two or more property cycles. Remember to use parentheses if writing
a multi-line `prop_cycle` parameter.

.. figure:: ../../gallery/color/images/sphx_glr_color_cycle_001.png
   :target: ../../gallery/color/color_cycle.html
   :align: center
   :scale: 50

   Color Cycle


New Colormaps
--------------

All four of the colormaps proposed as the new default are available
as ``'viridis'`` (the new default in 2.0), ``'magma'``, ``'plasma'``, and
``'inferno'``

.. plot::

   import numpy as np
   from cycler import cycler
   cmap = cycler('cmap', ['viridis', 'magma','plasma', 'inferno'])
   x_mode = cycler('x', [1, 2])
   y_mode = cycler('y', x_mode)

   cy = (x_mode * y_mode) + cmap

   def demo(ax, x, y, cmap):
       X, Y = np.ogrid[0:2*np.pi:200j, 0:2*np.pi:200j]
       data = np.sin(X*x) * np.cos(Y*y)
       ax.imshow(data, interpolation='none', cmap=cmap)
       ax.set_title(cmap)

   fig, axes = plt.subplots(2, 2)
   for ax, sty in zip(axes.ravel(), cy):
       demo(ax, **sty)

   fig.tight_layout()


Styles
------

Several new styles have been added, including many styles from the
Seaborn project.  Additionally, in order to prep for the upcoming 2.0
style-change release, a 'classic' and 'default' style has been added.
For this release, the 'default' and 'classic' styles are identical.
By using them now in your scripts, you can help ensure a smooth
transition during future upgrades of matplotlib, so that you can
upgrade to the snazzy new defaults when you are ready! ::

    import matplotlib.style
    matplotlib.style.use('classic')

The 'default' style will give you matplotlib's latest plotting styles::

    matplotlib.style.use('default')

Backends
--------

New backend selection
`````````````````````

The environment variable :envvar:`MPLBACKEND` can now be used to set the
matplotlib backend.


wx backend has been updated
```````````````````````````

The wx backend can now be used with both wxPython classic and
`Phoenix <http://wxpython.org/Phoenix/docs/html/main.html>`__.

wxPython classic has to be at least version 2.8.12 and works on Python 2.x. As
of May 2015 no official release of wxPython Phoenix is available but a
current snapshot will work on Python 2.7+ and 3.4+.

If you have multiple versions of wxPython installed, then the user code is
responsible setting the wxPython version.  How to do this is
explained in the comment at the beginning of the example
`examples\user_interfaces\embedding_in_wx2.py`.

Configuration (rcParams)
------------------------

Some parameters have been added, others have been improved.

+-------------------------+--------------------------------------------------+
| Parameter               | Description                                      |
+=========================+==================================================+
|`{x,y}axis.labelpad`     | mplot3d now respects these parameters            |
+-------------------------+--------------------------------------------------+
|`axes.labelpad`          | Default space between the axis and the label     |
+-------------------------+--------------------------------------------------+
|`errorbar.capsize`       | Default length of end caps on error bars         |
+-------------------------+--------------------------------------------------+
|`{x,y}tick.minor.visible`| Default visibility of minor x/y ticks            |
+-------------------------+--------------------------------------------------+
|`legend.framealpha`      | Default transparency of the legend frame box     |
+-------------------------+--------------------------------------------------+
|`legend.facecolor`       | Default facecolor of legend frame box (or        |
|                         | ``'inherit'`` from `axes.facecolor`)             |
+-------------------------+--------------------------------------------------+
|`legend.edgecolor`       | Default edgecolor of legend frame box (or        |
|                         | ``'inherit'`` from `axes.edgecolor`)             |
+-------------------------+--------------------------------------------------+
|`figure.titlesize`       | Default font size for figure suptitles           |
+-------------------------+--------------------------------------------------+
|`figure.titleweight`     | Default font weight for figure suptitles         |
+-------------------------+--------------------------------------------------+
|`image.composite_image`  | Whether a vector graphics backend should         |
|                         | composite several images into a single image or  |
|                         | not when saving. Useful when needing to edit the |
|                         | files further in Inkscape or other programs.     |
+-------------------------+--------------------------------------------------+
|`markers.fillstyle`      | Default fillstyle of markers. Possible values    |
|                         | are ``'full'`` (the default), ``'left'``,        |
|                         | ``'right'``, ``'bottom'``, ``'top'`` and         |
|                         | ``'none'``                                       |
+-------------------------+--------------------------------------------------+
|`toolbar`                | Added ``'toolmanager'`` as a valid value,        |
|                         | enabling the experimental ``ToolManager``        |
|                         | feature.                                         |
+-------------------------+--------------------------------------------------+


Widgets
-------

Active state of Selectors
`````````````````````````

All selectors now implement ``set_active`` and ``get_active`` methods (also
called when accessing the ``active`` property) to properly update and query
whether they are active.


Moved ``ignore``, ``set_active``, and ``get_active`` methods to base class ``Widget``
`````````````````````````````````````````````````````````````````````````````````````

Pushes up duplicate methods in child class to parent class to avoid duplication of code.


Adds enable/disable feature to MultiCursor
``````````````````````````````````````````

A MultiCursor object can be disabled (and enabled) after it has been created without destroying the object.
Example::

  multi_cursor.active = False


Improved RectangleSelector and new EllipseSelector Widget
`````````````````````````````````````````````````````````

Adds an `interactive` keyword which enables visible handles for manipulating the shape after it has been drawn.

Adds keyboard modifiers for:

- Moving the existing shape (default key = 'space')
- Making the shape square (default 'shift')
- Make the initial point the center of the shape (default 'control')
- Square and center can be combined

Allow Artists to Display Pixel Data in Cursor
`````````````````````````````````````````````

Adds `get_pixel_data` and `format_pixel_data` methods to artists
which can be used to add zdata to the cursor display
in the status bar.  Also adds an implementation for Images.


New plotting features
---------------------


Auto-wrapping Text
``````````````````

Added the keyword argument "wrap" to Text, which automatically breaks
long lines of text when being drawn.  Works for any rotated text,
different modes of alignment, and for text that are either labels or
titles.  This breaks at the ``Figure``, not ``Axes`` edge.

.. plot::

   fig, ax = plt.subplots()
   fig.patch.set_color('.9')
   ax.text(.5, .75,
           "This is a really long string that should be wrapped so that "
           "it does not go outside the figure.", wrap=True)

Contour plot corner masking
```````````````````````````

Ian Thomas rewrote the C++ code that calculates contours to add support for
corner masking.  This is controlled by a new keyword argument
``corner_mask`` in the functions :func:`~matplotlib.pyplot.contour` and
:func:`~matplotlib.pyplot.contourf`.  The previous behaviour, which is now
obtained using ``corner_mask=False``, was for a single masked point to
completely mask out all four quads touching that point.  The new behaviour,
obtained using ``corner_mask=True``, only masks the corners of those
quads touching the point; any triangular corners comprising three unmasked
points are contoured as usual.  If the ``corner_mask`` keyword argument is not
specified, the default value is taken from rcParams.

.. figure:: ../../gallery/images_contours_and_fields/images/sphx_glr_contour_corner_mask_001.png
   :target: ../../gallery/images_contours_and_fields/contour_corner_mask.html
   :align: center
   :scale: 50

   Contour Corner Mask


Mostly unified linestyles for `Line2D`, `Patch` and `Collection`
`````````````````````````````````````````````````````````````````

The handling of linestyles for Lines, Patches and Collections has been
unified.  Now they all support defining linestyles with short symbols,
like `"--"`, as well as with full names, like ``"dashed"``. Also the
definition using a dash pattern (``(0., [3., 3.])``) is supported for all
methods using `Line2D`, `Patch` or ``Collection``.


Legend marker order
```````````````````

Added ability to place the label before the marker in a legend box with
``markerfirst`` keyword


Support for legend for PolyCollection and stackplot
```````````````````````````````````````````````````

Added a `legend_handler` for :class:`~matplotlib.collections.PolyCollection` as well as a `labels` argument to
:func:`~matplotlib.axes.Axes.stackplot`.


Support for alternate pivots in mplot3d quiver plot
```````````````````````````````````````````````````

Added a :code:`pivot` kwarg to :func:`~mpl_toolkits.mplot3d.Axes3D.quiver`
that controls the pivot point around which the quiver line rotates. This also
determines the placement of the arrow head along the quiver line.


Logit Scale
```````````

Added support for the 'logit' axis scale, a nonlinear transformation

.. math::

   x -> \log10(x / (1-x))

for data between 0 and 1 excluded.


Add step kwargs to fill_between
```````````````````````````````

Added ``step`` kwarg to `Axes.fill_between` to allow to fill between
lines drawn using the 'step' draw style.  The values of ``step`` match
those of the ``where`` kwarg of `Axes.step`.  The asymmetry of of the
kwargs names is not ideal, but `Axes.fill_between` already has a
``where`` kwarg.

This is particularly useful for plotting pre-binned histograms.

.. figure:: ../../gallery/api/images/sphx_glr_filled_step_001.png
   :target: ../../gallery/api/filled_step.html
   :align: center
   :scale: 50

   Filled Step


Square Plot
```````````

Implemented square plots feature as a new parameter in the axis
function. When argument 'square' is specified, equal scaling is set,
and the limits are set such that ``xmax-xmin == ymax-ymin``.

.. plot::

   fig, ax = plt.subplots()
   ax.axis('square')


Updated figimage to take optional resize parameter
``````````````````````````````````````````````````

Added the ability to plot simple 2D-Array using ``plt.figimage(X, resize=True)``.
This is useful for plotting simple 2D-Array without the Axes or whitespacing
around the image.

.. plot::

   data = np.random.random([500, 500])
   plt.figimage(data, resize=True)

Updated Figure.savefig() can now use figure's dpi
`````````````````````````````````````````````````

Added support to save the figure with the same dpi as the figure on the
screen using `dpi='figure'`.

Example::

   f = plt.figure(dpi=25)               # dpi set to 25
   S = plt.scatter([1,2,3],[4,5,6])
   f.savefig('output.png', dpi='figure')    # output savefig dpi set to 25 (same as figure)


Updated Table to control edge visibility
````````````````````````````````````````

Added the ability to toggle the visibility of lines in Tables.
Functionality added to the :func:`pyplot.table` factory function under
the keyword argument "edges".  Values can be the strings "open", "closed",
"horizontal", "vertical" or combinations of the letters "L", "R", "T",
"B" which represent left, right, top, and bottom respectively.

Example::

    table(..., edges="open")  # No line visible
    table(..., edges="closed")  # All lines visible
    table(..., edges="horizontal")  # Only top and bottom lines visible
    table(..., edges="LT")  # Only left and top lines visible.

Zero r/cstride support in plot_wireframe
````````````````````````````````````````

Adam Hughes added support to mplot3d's plot_wireframe to draw only row or
column line plots.


.. plot::

    from mpl_toolkits.mplot3d import Axes3D, axes3d
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    X, Y, Z = axes3d.get_test_data(0.05)
    ax.plot_wireframe(X, Y, Z, rstride=10, cstride=0)


Plot bar and barh with labels
`````````````````````````````

Added kwarg ``"tick_label"`` to `bar` and `barh` to support plotting bar graphs with a
text label for each bar.

.. plot::

   plt.bar([1, 2], [.5, .75], tick_label=['bar1', 'bar2'],
           align='center')

Added center and frame kwargs to pie
````````````````````````````````````

These control where the center of the pie graph are and if
the Axes frame is shown.

Fixed 3D filled contour plot polygon rendering
``````````````````````````````````````````````

Certain cases of 3D filled contour plots that produce polygons with multiple
holes produced improper rendering due to a loss of path information between
:class:`~matplotlib.collections.PolyCollection` and
:class:`~mpl_toolkits.mplot3d.art3d.Poly3DCollection`.  A function
:func:`~matplotlib.collections.PolyCollection.set_verts_and_codes` was
added to allow path information to be retained for proper rendering.

Dense colorbars are rasterized
``````````````````````````````

Vector file formats (pdf, ps, svg) are efficient for
many types of plot element, but for some they can yield
excessive file size and even rendering artifacts, depending
on the renderer used for screen display.  This is a problem
for colorbars that show a large number of shades, as is
most commonly the case.  Now, if a colorbar is showing
50 or more colors, it will be rasterized in vector
backends.


DateFormatter strftime
``````````````````````
:class:`~matplotlib.dates.DateFormatter`s'
:meth:`~matplotlib.dates.DateFormatter.strftime` method will format
a :class:`datetime.datetime` object with the format string passed to
the formatter's constructor. This method accepts datetimes with years
before 1900, unlike :meth:`datetime.datetime.strftime`.


Artist-level {get,set}_usetex for text
``````````````````````````````````````

Add ``{get,set}_usetex`` methods to :class:`~matplotlib.text.Text` objects
which allow artist-level control of LaTeX rendering vs the internal mathtex
rendering.


`ax.remove()` works as expected
```````````````````````````````

As with artists added to an :class:`~matplotlib.axes.Axes`,
`Axes` objects can be removed from their figure via
:meth:`~matplotlib.axes.Axes.remove()`.


API Consistency fix within Locators set_params() function
`````````````````````````````````````````````````````````

:meth:`~matplotlib.ticker.Locator.set_params` function, which sets parameters
within a :class:`~matplotlib.ticker.Locator` type
instance, is now available to all `Locator` types. The implementation
also prevents unsafe usage by strictly defining the parameters that a
user can set.

To use, call ``set_params()`` on a `Locator` instance with desired arguments:
::

    loc = matplotlib.ticker.LogLocator()
    # Set given attributes for loc.
    loc.set_params(numticks=8, numdecs=8, subs=[2.0], base=8)
    # The below will error, as there is no such parameter for LogLocator
    # named foo
    # loc.set_params(foo='bar')


Date Locators
`````````````

Date Locators (derived from :class:`~matplotlib.dates.DateLocator`) now
implement the :meth:`~matplotlib.tickers.Locator.tick_values` method.
This is expected of all Locators derived from :class:`~matplotlib.tickers.Locator`.

The Date Locators can now be used easily without creating axes ::

    from datetime import datetime
    from matplotlib.dates import YearLocator
    t0 = datetime(2002, 10, 9, 12, 10)
    tf = datetime(2005, 10, 9, 12, 15)
    loc = YearLocator()
    values = loc.tick_values(t0, tf)

OffsetBoxes now support clipping
````````````````````````````````

`Artists` draw onto objects of type :class:`~OffsetBox`
through :class:`~OffsetBox.DrawingArea` and :class:`~OffsetBox.TextArea`.
The `TextArea` calculates the required space for the text and so the
text is always within the bounds, for this nothing has changed.

However, `DrawingArea` acts as a parent for zero or more `Artists` that
draw on it and may do so beyond the bounds. Now child `Artists` can be
clipped to the bounds of the `DrawingArea`.


OffsetBoxes now considered by tight_layout
``````````````````````````````````````````

When `~matplotlib.pyplot.tight_layout()` or `Figure.tight_layout()`
or `GridSpec.tight_layout()` is called, `OffsetBoxes` that are
anchored outside the axes will not get chopped out. The `OffsetBoxes` will
also not get overlapped by other axes in case of multiple subplots.

Per-page pdf notes in multi-page pdfs (PdfPages)
````````````````````````````````````````````````

Add a new method :meth:`~matplotlib.backends.backend_pdf.PdfPages.attach_note`
to the PdfPages class, allowing the
attachment of simple text notes to pages in a multi-page pdf of
figures. The new note is visible in the list of pdf annotations in a
viewer that has this facility (Adobe Reader, OSX Preview, Skim,
etc.). Per default the note itself is kept off-page to prevent it to
appear in print-outs.

`PdfPages.attach_note` needs to be called before `savefig()` in order to be
added to the correct figure.

Updated fignum_exists to take figure name
`````````````````````````````````````````

Added the ability to check the existence of a figure using its name
instead of just the figure number.
Example::

  figure('figure')
  fignum_exists('figure') #true


ToolManager
-----------

Federico Ariza wrote the new `~matplotlib.backend_managers.ToolManager`
that comes as replacement for `NavigationToolbar2`

`ToolManager` offers a new way of looking at the user interactions
with the figures.  Before we had the `NavigationToolbar2` with its own
tools like `zoom/pan/home/save/...` and also we had the shortcuts like
`yscale/grid/quit/....` `Toolmanager` relocate all those actions as
`Tools` (located in `~matplotlib.backend_tools`), and defines a way to
`access/trigger/reconfigure` them.

The `Toolbars` are replaced for `ToolContainers` that are just GUI
interfaces to `trigger` the tools. But don't worry the default
backends include a `ToolContainer` called `toolbar`


.. note::
    At the moment, we release this primarily for feedback purposes and should
    be treated as experimental until further notice as API changes will occur.
    For the moment the `ToolManager` works only with the `GTK3` and `Tk` backends.
    Make sure you use one of those.
    Port for the rest of the backends is comming soon.

    To activate the `ToolManager` include the following at the top of your file ::

      >>> matplotlib.rcParams['toolbar'] = 'toolmanager'


Interact with the ToolContainer
```````````````````````````````

The most important feature is the ability to easily reconfigure the ToolContainer (aka toolbar).
For example, if we want to remove the "forward" button we would just do. ::

 >>> fig.canvas.manager.toolmanager.remove_tool('forward')

Now if you want to programmatically trigger the "home" button ::

 >>> fig.canvas.manager.toolmanager.trigger_tool('home')


New Tools for ToolManager
`````````````````````````

It is possible to add new tools to the ToolManager

A very simple tool that prints "You're awesome" would be::

    from matplotlib.backend_tools import ToolBase
    class AwesomeTool(ToolBase):
        def trigger(self, *args, **kwargs):
            print("You're awesome")


To add this tool to `ToolManager`

 >>> fig.canvas.manager.toolmanager.add_tool('Awesome', AwesomeTool)

If we want to add a shortcut ("d") for the tool

 >>> fig.canvas.manager.toolmanager.update_keymap('Awesome', 'd')


To add it to the toolbar inside the group 'foo'

 >>> fig.canvas.manager.toolbar.add_tool('Awesome', 'foo')


There is a second class of tools, "Toggleable Tools", this are almost
the same as our basic tools, just that belong to a group, and are
mutually exclusive inside that group.  For tools derived from
`ToolToggleBase` there are two basic methods `enable` and `disable`
that are called automatically whenever it is toggled.


A full example is located in :ref:`sphx_glr_gallery_user_interfaces_toolmanager_sgskip.py`


cbook.is_sequence_of_strings recognizes string objects
------------------------------------------------------

This is primarily how pandas stores a sequence of strings ::

    import pandas as pd
    import matplotlib.cbook as cbook

    a = np.array(['a', 'b', 'c'])
    print(cbook.is_sequence_of_strings(a))  # True

    a = np.array(['a', 'b', 'c'], dtype=object)
    print(cbook.is_sequence_of_strings(a))  # True

    s = pd.Series(['a', 'b', 'c'])
    print(cbook.is_sequence_of_strings(s))  # True

Previously, the last two prints returned false.


New ``close-figs`` argument for plot directive
----------------------------------------------

Matplotlib has a sphinx extension ``plot_directive`` that creates plots for
inclusion in sphinx documents.  Matplotlib 1.5 adds a new option to the plot
directive - ``close-figs`` - that closes any previous figure windows before
creating the plots.  This can help avoid some surprising duplicates of plots
when using ``plot_directive``.

Support for URL string arguments to ``imread``
----------------------------------------------

The :func:`~matplotlib.pyplot.imread` function now accepts URL strings that
point to remote PNG files. This circumvents the generation of a
HTTPResponse object directly.

Display hook for animations in the IPython notebook
---------------------------------------------------

`~matplotlib.animation.Animation` instances gained a ``_repr_html_`` method
to support inline display of animations in the notebook. The method used
to display is controlled by the ``animation.html`` rc parameter, which
currently supports values of ``none`` and ``html5``. ``none`` is the
default, performing no display. ``html5`` converts the animation to an
h264 encoded video, which is embedded directly in the notebook.

Users not wishing to use the ``_repr_html_`` display hook can also manually
call the `to_html5_video` method to get the HTML and display using
IPython's ``HTML`` display class::

    from IPython.display import HTML
    HTML(anim.to_html5_video())

Prefixed pkg-config for building
--------------------------------

Handling of `pkg-config` has been fixed in so far as it is now
possible to set it using the environment variable `PKG_CONFIG`. This
is important if your toolchain is prefixed. This is done in a simpilar
way as setting `CC` or `CXX` before building. An example follows.

    export PKG_CONFIG=x86_64-pc-linux-gnu-pkg-config
