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Merge branch 'v2.x' into master
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‎ 10000 doc/_templates/index.html

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@@ -180,7 +180,7 @@ <h1>Third party packages</h1>
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<p>A large number of <a href="{{ pathto('thirdpartypackages/index') }}">third party packages</a>
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extend and build on Matplotlib functionality, including several higher-level plotting interfaces
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<a href="http://web.stanford.edu/~mwaskom/software/seaborn">seaborn</a>,
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<a href="https://seaborn.github.io/">seaborn</a>,
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<a href="http://holoviews.org">holoviews</a>,
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<a href="http://ggplot.yhathq.com">ggplot</a>, and
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two projection and mapping toolkits

‎doc/api/api_changes.rst

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@@ -155,6 +155,34 @@ where "ax" is an ``Axes3d`` object created with something like ::
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ax = plt.sublot(111, projection='3d')
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Changes in 1.5.3
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================
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``ax.plot(..., marker=None)`` gives default marker
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--------------------------------------------------
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Prior to 1.5.3 kwargs passed to `~matplotlib.Axes.plot` were handled
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in two parts -- default kwargs generated internal to
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`~matplotlib.Axes.plot` (such as the cycled styles) and user supplied
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kwargs. The internally generated kwargs were passed to the
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`matplotlib.lines.Line2D.__init__` and the user kwargs were passed to
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``ln.set(**kwargs)`` to update the artist after it was created. Now
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both sets of kwargs are merged and passed to
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`~matplotlib.lines.Line2D.__init__`. This change was made to allow `None`
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to be passed in via the user kwargs to mean 'do the default thing' as
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is the convention through out mpl rather than raising an exception.
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Unlike most `~matplotlib.lines.Line2D` setter methods
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`~matplotlib.lines.Line2D.set_marker` did accept `None` as a valid
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input which was mapped to 'no marker'. Thus, by routing this
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``marker=None`` through ``__init__`` rather than ``set(...)`` the meaning
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of ``ax.plot(..., marker=None)`` changed from 'no markers' to 'default markers
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from rcparams'.
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This is change is only evident if ``mpl.rcParams['lines.marker']`` has a value
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other than ``'None'`` (which is string ``'None'`` which means 'no marker').
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Changes in 1.5.2
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================
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‎doc/mpl_toolkits/index.rst

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@@ -73,3 +73,102 @@ Excel Tools
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``mpl_toolkits.exceltools`` provides some utilities for working with
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Excel. This toolkit ships with matplotlib, but requires
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`xlwt <http://pypi.python.org/pypi/xlwt>`_
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.. _toolkit_natgrid:
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Natgrid
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=======
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(*Not distributed with matplotlib*)
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mpl_toolkits.natgrid is an interface to natgrid C library for gridding
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irregularly spaced data. This requires a separate installation of the
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`natgrid toolkit <http://github.com/matplotlib/natgrid>`__.
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.. _toolkit_matplotlibvenn:
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Matplotlib-Venn
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===============
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(*Not distributed with matplotlib*)
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`Matplotlib-Venn <https://github.com/konstantint/matplotlib-venn>`_ provides a set of functions for plotting 2- and 3-set area-weighted (or unweighted) Venn diagrams.
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mplstereonet
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===============
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(*Not distributed with matplotlib*)
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`mplstereonet <https://github.com/joferkington/mplstereonet>`_ provides stereonets for plotting and analyzing orientation data in Matplotlib.
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.. _hl_plotting:
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High-Level Plotting
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*******************
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Several projects have started to provide a higher-level interface to
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matplotlib. These are independent projects.
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.. _toolkit_seaborn:
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seaborn
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=======
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(*Not distributed with matplotlib*)
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`seaborn <https://seaborn.github.io/>`_ is a high level interface for drawing
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statistical graphics with matplotlib. It aims to make visualization a central
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part of exploring and understanding complex datasets.
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.. image:: /_static/seaborn.png
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:height: 157px
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.. _toolkit_holoviews:
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holoviews
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=========
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(*Not distributed with matplotlib*)
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`holoviews <http://holoviews.org>`_ makes it easier to visualize data
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interactively, especially in a `Jupyter notebook
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<http://jupyter.org>`_, by providing a set of declarative
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plotting objects that store your data and associated metadata. Your
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data is then immediately visualizable alongside or overlaid with other
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data, either statically or with automatically provided widgets for
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parameter exploration.
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.. image:: /_static/holoviews.png
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:height: 354px
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.. _toolkit_ggplot:
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ggplot
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======
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(*Not distributed with matplotlib*)
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`ggplot <https://github.com/yhat/ggplot>`_ is a port of the R ggplot2
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to python based on matplotlib.
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.. image:: /_static/ggplot.png
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:height: 195px
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.. _toolkit_prettyplotlib:
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prettyplotlib
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=============
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(*Not distributed with matplotlib*)
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`prettyplotlib <https://olgabot.github.io/prettyplotlib>`_ is an extension
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to matplotlib which changes many of the defaults to make plots some
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consider more attractive.
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iTerm2 terminal backend
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=======================
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(*Not distributed with matplotlib*)
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`matplotlib_iterm2 <https://github.com/oselivanov/matplotlib_iterm2>`_ is an
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external matplotlib backend uses iTerm2 nightly build inline image display
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feature.
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.. image:: /_static/matplotlib_iterm2_demo.png

‎lib/matplotlib/tests/test_contour.py

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from matplotlib.testing.decorators import cleanup, image_comparison
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from matplotlib import pyplot as plt
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from nose.tools import assert_equal, assert_raises
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from numpy.testing import assert_array_almost_equal
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import warnings
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import re
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assert str(w[0].message).startswith(msg)
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@cleanup
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def test_contourf_symmetric_locator():
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# github issue 7271
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z = np.arange(12).reshape((3, 4))
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locator = plt.MaxNLocator(nbins=4, symmetric=True)
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cs = plt.contourf(z, locator=locator)
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assert_array_almost_equal(cs.levels, np.linspace(-12, 12, 5))
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if __name__ == '__main__':
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import nose
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nose.runmodule(argv=['-s', '--with-doctest'], exit=False)

‎lib/matplotlib/ticker.py

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return self.tick_values(vmin, vmax)
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def tick_values(self, vmin, vmax):
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if self._symmetric:
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vmax = max(abs(vmin), abs(vmax))
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vmin = -vmax
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vmin, vmax = mtransforms.nonsingular(
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vmin, vmax, expander=1e-13, tiny=1e-14)
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locs = self._raw_ticks(vmin, vmax)
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prune = self._prune
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if prune == 'lower':
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locs = locs[1:]
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def view_limits(self, dmin, dmax):
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if self._symmetric:
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maxabs = max(abs(dmin), abs(dmax))
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dmin = -maxabs
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dmax = maxabs
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dmax = max(abs(dmin), abs(dmax))
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dmin = -dmax
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dmin, dmax = mtransforms.nonsingular(
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dmin, dmax, expander=1e-12, tiny=1e-13)

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