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Merge pull request matplotlib#2 from matplotlib/master
Update to matplotlib:master
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‎CHANGELOG‎

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2014-05-02 Added colorblind-friendly colormap, named 'Wistia'.
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2014-04-27 Improved input clean up in Axes.{h|v}lines
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Coerce input into a 1D ndarrays (after dealing with units).
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2014-04-22 Added an example showing the difference between
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interpolation = 'none' and interpolation = 'nearest' in
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`imshow()` when saving vector graphics files.
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2014-04-10 Fixed the triangular marker rendering error. The "Up" triangle was
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rendered instead of "Right" triangle and vice-versa.
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2014-04-08 Fixed a bug in parasite_axes.py by making a list out
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of a generator at line 263.
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2014-03-27 Added tests for pie ccw parameter. Removed pdf and svg images
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from tests for pie linewidth parameter.
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2014-03-24 Added bool kwarg (manage_xticks) to boxplot to enable/disable
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the managemnet of the xlimits and ticks when making a boxplot.
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Default in True which maintains current behavior by default.
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2014-03-22 Added the keyword arguments wedgeprops and textprops to pie.
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Users can control the wedge and text properties of the pie
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in more detail, if they choose.
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2014-03-13 Add parameter 'clockwise' to function pie, True by default.
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2014-27-02 Implemented separate horizontal/vertical axes padding to the
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2014-02-27 Implemented separate horizontal/vertical axes padding to the
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ImageGrid in the AxesGrid toolkit
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2014-02-25 In backend_qt4agg changed from using update -> repaint under
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windows. See comment in source near `self._priv_update` for
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longer explaination.
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2014-01-02 `triplot` now returns the artist it adds and support of line and
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marker kwargs has been improved. GBY
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all pyplot.tri* methods) and mlab.griddata. Deprecated
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matplotlib.delaunay module. - IMT
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2013-11-05 Add power-law normalization method. This is useful for,
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e.g., showing small populations in a "hist2d" histogram.
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2013-10-27 Added get_rlabel_position and set_rlabel_position methods to
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PolarAxes to control angular position of radial tick labels.
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‎INSTALL‎

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==================
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These are external packages which you will need to install before
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installing matplotlib. Windows users only need the first two (python
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and numpy) since the others are built into the matplotlib Windows
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installers available for download at `the download page
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<http://matplotlib.org/downloads.html>`_. If you are
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building on OSX, see :ref:`build_osx`. If you are installing
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dependencies with a package manager on Linux, you may need to install
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the development packages (look for a "-dev" postfix) in addition to
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the libraries themselves.
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installing matplotlib. If you are building on OSX, see
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:ref:`build_osx`. If you are installing dependencies with a package
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manager on Linux, you may need to install the development packages
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(look for a "-dev" postfix) in addition to the libraries themselves.
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.. note::
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using pip, easy_install or installing from source, the installer
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will attempt to download and install `pyparsing` from PyPI.
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<<<<<<< HEAD
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**Optional dependencies**
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six 1.3 or later
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Python 2/3 compatibility library. This is also a dependency of
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:term:`dateutil`.
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`Pillow http://python-imaging.github.io/`__
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If Pillow is installed, matplotlib can read and write a larger
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selection of image file formats.
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**Optional GUI frameworks**
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=======
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libpng 1.2 (or later)
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library for loading and saving :term:`PNG` files (`download
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<http://www.libpng.org/pub/png/libpng.html>`__). libpng requires
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zlib. If you are a Windows user, you can ignore this because we
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build support into the matplotlib single-click installer
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:term:`freetype` 1.4 (or later)
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library for reading true type font files. If you are a windows
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user, you can ignore this since we build support into the
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matplotlib single click installer.
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**Optional**
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>>>>>>> upstream/v1.3.x
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These are optional packages which you may want to install to use
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matplotlib with a user interface toolkit. See
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The python wrappers for the wx widgets library for use with the
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WX or WXAgg backend
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**Optional external programs**
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ffmpeg/avconv or mencoder
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Required for the animation module to be save out put to movie
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formats.
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ImageMagick
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Required for the animation module to be able to save to animated gif.
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**Optional dependencies**
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`Pillow http://python-imaging.github.io/`__
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If Pillow is installed, matplotlib can read and write a larger
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selection of image file formats.
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:term:`freetype` 1.4 (or later)
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library for reading true type font files. If you are a windows
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user, you can ignore this since we build support into the
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matplotlib single click installer.
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libpng 1.2 (or later)
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library for loading and saving :term:`PNG` files (`download
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<http://www.libpng.org/pub/png/libpng.html>`__). libpng requires
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zlib. If you are a Windows user, you can ignore this because we
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build support into the matplotlib single-click installer
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**Required libraries that ship with matplotlib**
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:term:`agg` 2.4
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`PyCXX` 6.2.4
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A library for writing Python extensions in C++.
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`qhull` 2012.1
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A library for computing convex hulls. Used for computing triangulation
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and meshes.
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`ttconv`
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truetype font utility
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.. _build_osx:
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Building on OSX

‎doc/api/api_changes.rst‎

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Changes in 1.4.x
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================
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Code changes
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------------
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* A major refactoring of the axes module was made. The axes module has been
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split into smaller modules:
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- mstream -> `from matplotlib import stream as mstream`
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- mtable -> `from matplotlib import table as mtable`
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* The :func:`~matplotlib.pyplot.errorbar` method has been changed such that
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the upper and lower limits (*lolims*, *uplims*, *xlolims*, *xuplims*) now
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point in the correct direction.
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* The Sphinx extensions `ipython_directive` and
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`ipython_console_highlighting` have been moved to the IPython
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project itself. While they remain in matplotlib for this release,
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* Removed the class `FigureManagerQTAgg` and deprecated `NavigationToolbar2QTAgg`
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which will be removed in 1.5.
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* Removed formerly public (non-prefixed) attributes `rect` and
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`drawRect` from `FigureCanvasQTAgg`; they were always an
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implementation detail of the (preserved) `drawRectangle()` function.
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* The function signatures of `tight_bbox.adjust_bbox` and
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`tight_bbox.process_figure_for_rasterizing` have been changed. A new
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`fixed_dpi` parameter allows for overriding the `figure.dpi` setting
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* Added clockwise parameter to control sectors direction in `axes.pie`
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Code removal
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------------
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* Removed ``mlab.levypdf``. The code raised a numpy error (and has for
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a long time) and was not the standard form of the Levy distribution.
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``scipy.stats.levy`` should be used instead
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.. _changes_in_1_3:
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‎doc/faq/installing_faq.rst‎

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When you run `git pull`, if the output shows that only Python files have been
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updated, you are all set. If C files have changed, you need to run the `python
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setupegg.py develop` command again to compile them.
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setup.py develop` command again to compile them.
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There is more information on :ref:`using git <using-git>` in
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the developer docs.

‎doc/users/customizing.rst‎

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import matplotlib as mpl
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mpl.rc('lines', linewidth=2, color='r')
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There :func:`matplotlib.rcdefaults` command will restore the standard
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matplotlib default settings.
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The :func:`matplotlib.rcdefaults` command will restore the standard matplotlib
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default settings.
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There is some degree of validation when setting the values of rcParams, see
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:mod:`matplotlib.rcsetup` for details.

‎doc/users/whats_new.rst‎

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new in matplotlib-1.4
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=====================
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New colormap
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------------
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In heatmaps, a green-to-red spectrum is often used to indicate intensity of
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activity, but this can be problematic for the red/green colorblind. A new,
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colorblind-friendly colormap is now available at :class:`matplotlib.cm.Wistia`.
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This colormap maintains the red/green symbolism while achieving deuteranopic
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legibility through brightness variations. See
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`here <https://github.com/wistia/heatmap-palette>`
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for more information.
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Documentation changes
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New plotting features
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---------------------
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Power-law normalization
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```````````````````````
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Ben Gamari added a power-law normalization method,
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:class:`~matplotlib.colors.PowerNorm`. This class maps a range of
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values to the interval [0,1] with power-law scaling with the exponent
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provided by the constructor's `gamma` argument. Power law normalization
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can be useful for, e.g., emphasizing small populations in a histogram.
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Fully customizable boxplots
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````````````````````````````
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Paul Hobson overhauled the :func:`~matplotlib.pyplot.boxplot` method such
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:ref:`~examples/statistics/boxplot_demo.py` and
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:ref:`~examples/statistics/bxp_demo.py`
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Added a bool kwarg, `manage_xticks`, which if False disables the management
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of the xtick and xlim by `boxplot`.
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Support for datetime axes in 2d plots
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`````````````````````````````````````
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the wedges in the pie. For more properties that the user can specify, look at
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the docs for the wedge and text objects.
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Fixed the direction of errorbar upper/lower limits
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``````````````````````````````````````````````````
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Larry Bradley fixed the :func:`~matplotlib.pyplot.errorbar` method such
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that the upper and lower limits (*lolims*, *uplims*, *xlolims*,
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*xuplims*) now point in the correct direction.
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Date handling
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-------------
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dialog. It was previously a QMainWindow, leaving the SPT open if one closed the
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plotwindow.
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Interactive performance of the Qt4 backend has been dramatically improved
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under windows.
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Cairo backends
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saved figures using the url kwarg of the :class:`~matplotlib.text.Text`
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class.
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Sphinx extensions
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-----------------
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The ``:context:`` directive in the `~matplotlib.sphinxext.plot_directive`
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Sphinx extension can now accept an optional ``reset`` setting, which will
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cause the context to be reset. This allows more than one distinct context to
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be present in documentation. To enable this option, use ``:context: reset``
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instead of ``:context:`` any time you want to reset the context.
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.. _whats-new-1-3:
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‎examples/api/power_norm_demo.py‎

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#!/usr/bin/python
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from matplotlib import pyplot as plt
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import matplotlib.colors as mcolors
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import numpy as np
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from numpy.random import multivariate_normal
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data = np.vstack([multivariate_normal([10, 10], [[3, 5],[4, 2]], size=100000),
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multivariate_normal([30, 20], [[2, 3],[1, 3]], size=1000)
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])
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gammas = [0.8, 0.5, 0.3]
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xgrid = np.floor((len(gammas) + 1.) / 2)
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ygrid = np.ceil((len(gammas) + 1.) / 2)
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plt.subplot(xgrid, ygrid, 1)
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plt.title('Linear normalization')
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plt.hist2d(data[:,0], data[:,1], bins=100)
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for i, gamma in enumerate(gammas):
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plt.subplot(xgrid, ygrid, i + 2)
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plt.title('Power law normalization\n$(\gamma=%1.1f)$' % gamma)
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plt.hist2d(data[:, 0], data[:, 1],
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bins=100, norm=mcolors.PowerNorm(gamma))
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plt.subplots_adjust(hspace=0.39)
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plt.show()

‎examples/images_contours_and_fields/interpolation_none_vs_nearest.py‎

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"""
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Displays the difference between interpolation = 'none' and
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interpolation = 'nearest'.
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Interpolation = 'none' and interpolation = 'nearest' are equivalent when
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converting a figure to an image file, such as a PNG.
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Interpolation = 'none' and interpolation = 'nearest' behave quite
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differently, however, when converting a figure to a vector graphics file,
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such as a PDF. As shown, Interpolation = 'none' works well when a big
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image is scaled down, while interpolation = 'nearest' works well when a
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small image is blown up.
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.cbook as cbook
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#Load big image
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big_im_path = cbook.get_sample_data('necked_tensile_specimen.png')
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big_im = plt.imread(big_im_path)
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#Define small image
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small_im = np.array([[0.25, 0.75, 1.0, 0.75], [0.1, 0.65, 0.5, 0.4], \
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[0.6, 0.3, 0.0, 0.2], [0.7, 0.9, 0.4, 0.6]])
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#Create a 2x2 table of plots
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fig = plt.figure(figsize = [8.0, 7.5])
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ax = plt.subplot(2,2,1)
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ax.imshow(big_im, interpolation = 'none')
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ax = plt.subplot(2,2,2)
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ax.imshow(big_im, interpolation = 'nearest')
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ax = plt.subplot(2,2,3)
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ax.imshow(small_im, interpolation = 'none')
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ax = plt.subplot(2,2,4)
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ax.imshow(small_im, interpolation = 'nearest')
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plt.subplots_adjust(left = 0.24, wspace = 0.2, hspace = 0.1, \
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bottom = 0.05, top = 0.86)
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#Label the rows and columns of the table
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fig.text(0.03, 0.645, 'Big Image\nScaled Down', ha = 'left')
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fig.text(0.03, 0.225, 'Small Image\nBlown Up', ha = 'left')
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fig.text(0.383, 0.90, "Interpolation = 'none'", ha = 'center')
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fig.text(0.75, 0.90, "Interpolation = 'nearest'", ha = 'center')
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#If you were going to run this example on your local machine, you
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#would save the figure as a PNG, save the same figure as a PDF, and
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#then compare them. The following code would suffice.
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txt = fig1.text(0.452, 0.95, 'Saved as a PNG', fontsize = 18)
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# plt.savefig('None_vs_nearest-png.png')
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# txt.set_text('Saved as a PDF')
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# plt.savefig('None_vs_nearest-pdf.pdf')
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#Here, however, we need to display the PDF on a webpage, which means
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#the PDF must be converted into an image. For the purposes of this
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#example, the 'Nearest_vs_none-pdf.pdf' has been pre-converted into
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#'Nearest_vs_none-pdf.png' at 80 dpi. We simply need to load and
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#display it.
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pdf_im_path = cbook.get_sample_data('None_vs_nearest-pdf.png')
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pdf_im = plt.imread(pdf_im_path)
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fig2 = plt.figure(figsize = [8.0, 7.5])
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plt.figimage(pdf_im)
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plt.show()

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