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Merge remote-tracking branch 'matplotlib/v2.1.0-doc' into v2.1.x
2 parents 83547cb + 2d7fa01 commit 26c14a3

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‎.appveyor.yml‎

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# same things as the requirements in ci/conda_recipe/meta.yaml
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# if conda-forge gets a new pyqt, it might be nice to install it as well to have more backends
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# https://github.com/conda-forge/conda-forge.github.io/issues/157#issuecomment-223536381
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#
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# n.b. pandas is pinned to <0.21 due to issue 9610
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- conda create -q -n test-environment python=%PYTHON_VERSION%
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pip setuptools numpy python-dateutil freetype=2.6 msinttypes "tk=8.5"
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pyparsing pytz tornado "libpng>=1.6.21,<1.7" "zlib=1.2" "cycler>=0.10"
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mock sphinx pandas
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mock sphinx "pandas<0.21.0"
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- activate test-environment
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- echo %PYTHON_VERSION% %TARGET_ARCH%
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- if %PYTHON_VERSION% == 2.7 conda install -q backports.functools_lru_cache

‎doc/_static/mpl-scatter-density.png‎

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‎doc/devel/contributing.rst‎

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You can check out the latest sources with the command (see
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:ref:`set-up-fork` for more details)::
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git clone https://github.com:matplotlib/matplotlib.git
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git clone https://github.com/matplotlib/matplotlib.git
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and navigate to the :file:`matplotlib` directory. If you have the proper privileges,
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you can use ``git@`` instead of ``https://``, which works through the ssh protocol
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3. Clone this copy to your local disk::
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$ git clone https://github.com:YourLogin/matplotlib.git
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$ git clone https://github.com/YourLogin/matplotlib.git
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4. Create a branch to hold your changes::
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‎doc/devel/gitwash/development_workflow.rst‎

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Now all those people can do::
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git clone https://gith hub.com:your-user-name/matplotlib.git
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git clone https://github.com/your-user-name/matplotlib.git
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Remember that links starting with ``https`` or ``git@`` are read-write, and that
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``git@`` uses the ssh protocol; links starting with ``git://`` are read-only.

‎doc/devel/gitwash/set_up_fork.rst‎

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::
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git clone https://github.com:your-user-name/matplotlib.git
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git clone https://github.com/your-user-name/matplotlib.git
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cd matplotlib
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git remote add upstream git://github.com/matplotlib/matplotlib.git
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---------------
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#. Clone your fork to the local computer with ``git clone
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https://github.com:your-user-name/matplotlib.git``
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https://github.com/your-user-name/matplotlib.git``
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#. Investigate. Change directory to your new repo: ``cd matplotlib``. Then
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``git branch -a`` to show you all branches. You'll get something
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like:
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upstream git://github.com/matplotlib/matplotlib.git (fetch)
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upstream git://github.com/matplotlib/matplotlib.git (push)
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origin https://github.com:your-user-name/matplotlib.git (fetch)
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origin https://github.com:your-user-name/matplotlib.git (push)
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origin https://github.com/your-user-name/matplotlib.git (fetch)
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origin https://github.com/your-user-name/matplotlib.git (push)
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.. include:: links.inc

‎doc/thirdpartypackages/index.rst‎

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Specialty plots
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***************
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DeCiDa
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======
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`DeCiDa <https://pypi.python.org/pypi/DeCiDa>`_ is a library of functions
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and classes for electron device characterization, electronic circuit design and
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general data visualization and analysis.
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Matplotlib-Venn
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===============
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`Matplotlib-Venn <https://github.com/konstantint/matplotlib-venn>`_ provides a
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.. image:: /_static/probscale_demo.png
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mpl-scatter-density
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===================
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`mpl-scatter-density <https://github.com/astrofrog/mpl-scatter-density>`_ is a
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small package that makes it easy to make scatter plots of large numbers
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of points using a density map. The following example contains around 13 million
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points and the plotting (excluding reading in the data) took less than a
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second on an average laptop:
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.. image:: /_static/mpl-scatter-density.png
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:height: 400px
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When used in interactive mode, the density map is downsampled on-the-fly while
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panning/zooming in order to provide a smooth interactive experience.
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mplstereonet
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============
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`mplstereonet <https://github.com/joferkington/mplstereonet>`_ provides
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Numpy library to manage wind data, draw windroses (also known as polar rose
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plots), draw probability density functions and fit Weibull distributions.
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DeCiDa
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======
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`DeCiDa <https://pypi.python.org/pypi/DeCiDa>`_ is a library of functions
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and classes for electron device characterization, electronic circuit design and
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general data visualization and analysis.
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Interactivity
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*************
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doc/users/intro.rst renamed to doc/users/history.rst

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Introduction
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============
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History
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=======
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.. note::
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The following introductory text was written in 2008 by John D. Hunter
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(1968-2012), the original author of Matplotlib.
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Matplotlib is a library for making 2D plots of arrays in `Python
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<https://www.python.org>`_. Although it has its origins in emulating
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the MATLAB |reg| [#]_ graphics commands, it is
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the MATLAB graphics commands, it is
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independent of MATLAB, and can be used in a Pythonic, object oriented
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way. Although Matplotlib is written primarily in pure Python, it
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makes heavy use of `NumPy <http://www.numpy.org>`_ and other extension
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code to provide good performance even for large arrays.
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.. |reg| unicode:: 0xAE
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:ltrim:
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Matplotlib is designed with the philosophy that you should be able to
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create simple plots with just a few commands, or just one! If you
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want to see a histogram of your data, you shouldn't need to
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*pylab interface* is the set of functions provided by
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:mod:`matplotlib.pylab` which allow the user to create plots with code
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quite similar to MATLAB figure generating code
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(:ref:`sphx_glr_tutorials_introductory_pyplot.py`). The *Matplotlib frontend* or *Matplotlib
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(:doc:`/tutorials/introductory/pyplot`). The *Matplotlib frontend* or *Matplotlib
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API* is the set of classes that do the heavy lifting, creating and
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managing figures, text, lines, plots and so on
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(:ref:`sphx_glr_tutorials_intermediate_artists.py`). This is an abstract interface that knows
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(:doc:`/tutorials/intermediate/artists`). This is an abstract interface that knows
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nothing about output. The *backends* are device-dependent drawing
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devices, aka renderers, that transform the frontend representation to
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hardcopy or a display device (:ref:`what-is-a-backend`). Example
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to a printer or publishers. Others deploy Matplotlib on a web
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application server to generate PNG output for inclusion in
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dynamically-generated web pages. Some use Matplotlib interactively
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from the Python shell in Tkinter on Windows™. My primary use is to
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from the Python shell in Tkinter on Windows. My primary use is to
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embed Matplotlib in a Gtk+ EEG application that runs on Windows, Linux
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and Macintosh OS X.
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.. [#] MATLAB is a registered trademark of The MathWorks, Inc.
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----
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Matplotlib's original logo (2003 -- 2008).
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..
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The original logo was added in fc8c215.
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.. plot::
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from matplotlib import cbook, pyplot as plt, style
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import numpy as np
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style.use("classic")
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datafile = cbook.get_sample_data('membrane.dat', asfileobj=False)
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# convert data to mV
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x = 1000 * 0.1 * np.fromstring(open(datafile, 'rb').read(), np.float32)
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# 0.0005 is the sample interval
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t = 0.0005 * np.arange(len(x))
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plt.figure(1, figsize=(7, 1), dpi=100)
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ax = plt.subplot(111, facecolor='y')
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plt.plot(t, x)
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plt.text(0.5, 0.5, 'matplotlib', color='r',
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fontsize=40, fontname=['Courier', 'DejaVu Sans Mono'],
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horizontalalignment='center',
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verticalalignment='center',
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transform=ax.transAxes,
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)
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plt.axis([1, 1.72, -60, 10])
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plt.gca().set_xticklabels([])
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plt.gca().set_yticklabels([])

‎doc/users/index.rst‎

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.. toctree::
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intro.rst
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history.rst
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installing.rst
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../tutorials/index.rst
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interactive.rst

‎examples/animation/animate_decay.py‎

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Decay
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=====
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This example showcases a sinusoidal decay animation.
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A sinusoidal decay animation.
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"""
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‎examples/animation/basic_example.py‎

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Simple animation examples
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=========================
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This example contains two animations. The first is a random walk plot. The
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second is an image animation.
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Two animations where the first is a random walk plot and
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the second is an image animation.
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"""
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import numpy as np

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