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‎INSTALL

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.. The source of this document is INSTALL. During the doc build process,
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.. this file is copied over to doc/users/installing.rst.
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.. Therefore, you must edit INSTALL, *not* doc/users/installing.rst!
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.. _pip: https://pypi.python.org/pypi/pip/
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**********
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Installing
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The first option is to use one of the pre-packaged python
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distributions that already provide matplotlib built-in. The
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Continuum.io Python distribution (`Anaconda
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<https://store.continuum.io/cshop/anaconda/>`_ or `miniconda
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<https://www.continuum.io/downloads/>`_ or `miniconda
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<http://conda.pydata.org/miniconda.html>`_) and the Enthought
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distribution `(Canopy) <https://www.enthought.com/products/canopy/>`_
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are both excellent choices that "just work" out of the box for
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Windows, OSX and common Linux platforms. Both of these distributions
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include matplotlib and *lots* of other useful tools. Another
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excellent alternative for Windows users is `Python (x, y)
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<https://code.google.com/p/pythonxy>`_ .
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include matplotlib and *lots* of other useful tools.
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Linux : using your package manager
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-------------------
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If you are on Mac OSX you can probably install matplotlib binaries using the
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standard Python installation program `pip <https://pypi.python.org/pypi/pip>`_.
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standard Python installation program pip_.
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See :ref:`install_osx_binaries`.
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.. _installing_windows:
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Windows
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-------
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If you don't already have Python installed, we recommend using
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one of the `scipy-stack compatible Python distributions
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<http://www.scipy.org/install.html>`_ such as WinPython, Python(x,y),
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Enthought Canopy, or Continuum Anaconda, which have matplotlib and
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many of its dependencies, plus other useful packages, preinstalled.
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For `standard Python <http://www.python.org/download/>`_ installations
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you will also need to install compatible versions of
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`setuptools <https://pypi.python.org/pypi/setuptools/>`_,
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`numpy <https://pypi.python.org/pypi/numpy/>`_,
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`python-dateutil <https://pypi.python.org/pypi/python-dateutil/>`_,
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`pytz <https://pypi.python.org/pypi/pytz>`_,
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`pyparsing <https://pypi.python.org/pypi/pyparsing/>`_, and
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`cycler <https://pypi.python.org/pypi/Cycler>`_
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in addition to
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`matplotlib <http://pypi.python.org/pypi/matplotlib/>`_.
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For Python 3.5 the `Visual C++ Redistributable for Visual Studio 2015
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<http://www.microsoft.com/en-us/download/details.aspx?id=48145>`_
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needs to be installed.
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In case Python 2.7 to 3.4 are not installed for all users (not the default),
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Enthought Canopy, or Continuum Anaconda, which have matplotlib and many
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of its dependencies, plus other useful packages, preinstalled.
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For `standard Python <https://www.python.org/download/>`_ installations,
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install matplotlib using pip_::
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python -m pip install -U pip setuptools
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python -m pip install matplotlib
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In case Python 2.7 or 3.4 are not installed for all users,
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the Microsoft Visual C++ 2008 (
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`64 bit <http://www.microsoft.com/download/en/details.aspx?id=15336>`__
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or
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`32 bit <http://www.microsoft.com/download/en/details.aspx?id=29>`__
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for Python 2.7 to 3.2) or Microsoft Visual C++ 2010 (
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for Python 2.7) or Microsoft Visual C++ 2010 (
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`64 bit <http://www.microsoft.com/en-us/download/details.aspx?id=14632>`__
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or
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`32 bit <http://www.microsoft.com/en-us/download/details.aspx?id=5555>`__
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for Python 3.3 and 3.4) redistributable packages need to be installed.
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for Python 3.4) redistributable packages need to be installed.
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Matplotlib depends on `Pillow <https://pypi.python.org/pypi/Pillow>`_
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for reading and saving JPEG, BMP, and TIFF image files.
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or GhostScript.
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TkAgg is probably the best backend for interactive use from the
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standard Python shell or IPython. It is enabled as the default backend
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standard Python shell or IPython. It is enabled as the default backend
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for the official binaries. GTK3 is not supported on Windows.
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The Windows installers (:file:`*.exe`) and wheels (:file:`*.whl`) on
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the `PyPI download page <http://pypi.python.org/pypi/matplotlib/>`_ do
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not contain test data or example code. If you want to try the many
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demos that come in the matplotlib source distribution, download the
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:file:`*.tar.gz` file and look in the :file:`examples` subdirectory.
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To run the test suite, copy the :file:`lib\matplotlib\tests` and
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:file:`lib\mpl_toolkits\tests` directories from the source
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distribution to :file:`sys.prefix\Lib\site-packages\matplotlib` and
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:file:`sys.prefix\Lib\site-packages\mpl_toolkits` respectively, and
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The Windows wheels (:file:`*.whl`) on the `PyPI download page
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<https://pypi.python.org/pypi/matplotlib/>`_ do not contain test data
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or example code.
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If you want to try the many demos that come in the matplotlib source
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distribution, download the :file:`*.tar.gz` file and look in the
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:file:`examples` subdirectory.
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To run the test suite, copy the :file:`lib\\matplotlib\\tests` and
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:file:`lib\\mpl_toolkits\\tests` directories from the source
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distribution to :file:`sys.prefix\\Lib\\site-packages\\matplotlib` and
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:file:`sys.prefix\\Lib\\site-packages\\mpl_toolkits` respectively, and
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install `nose <https://pypi.python.org/pypi/nose>`_, `mock
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<https://pypi.python.org/pypi/mock>`_, Pillow, MiKTeX, GhostScript,
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ffmpeg, avconv, mencoder, ImageMagick, and `Inkscape
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<http://inkscape.org/>`_.
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<https://inkscape.org/>`_.
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.. _build_windows:
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Building on Windows
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-------------------
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The Python shipped from http://www.python.org is compiled with Visual Studio
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2008 for versions before 3.3 and Visual Studio 2010 for 3.3 and later. Python
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extensions are recommended to be compiled with the same compiler. The .NET
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Framework 4.0 is required for MSBuild (you'll likely have the requisite
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Framework with Visual Studio). In addition to Visual Studio `CMake
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<http://www.cmake.org>`_ is required for building libpng.
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Since there is no canonical Windows package manager the build methods for
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freetype, zlib, libpng, tcl, & tk source code are documented as a build script
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2008 for versions before 3.3, Visual Studio 2010 for 3.3 and 3.4, and
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Visual Studio 2015 for 3.5. Python extensions are recommended to be compiled
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with the same compiler.
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Since there is no canonical Windows package manager, the methods for building
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freetype, zlib, and libpng from source code are documented as a build script
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at `matplotlib-winbuild <https://github.com/jbmohler/matplotlib-winbuild>`_.

‎doc/_static/logo2.png

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‎doc/_templates/index.html

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You can generate plots, histograms, power spectra, bar charts,
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errorcharts, scatterplots, etc, with just a few lines of code.
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For a sampling, see the <a href="{{ pathto('users/screenshots') }}">screenshots</a>, <a href="{{ pathto('gallery') }}">thumbnail</a> gallery, and
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<a href="examples/index.html">examples</a> directory</p>
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<a href="{{ pathto('examples/index') }}">examples</a> directory</p>
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<p>For simple plotting the <tt>pyplot</tt> interface provides a
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MATLAB-like interface, particularly when combined
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<h1>Installation</h1>
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Visit the
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<a href="http://matplotlib.org/users/installing.html">matplotlib
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installation instructions.</a>.
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Visit the <a href="{{ pathto('users/installing') }}">matplotlib installation instructions</a>.
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<h1>Documentation</h1>
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</script>
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<p>Trying to learn how to do a particular kind of plot? Check out
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the <a href="gallery.html">gallery</a>, <a href="examples/index.html">examples</a>,
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or the <a href="api/pyplot_summary.html">list of plotting
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the <a href="{{ pathto('gallery') }}">gallery</a>, <a href="{{ pathto('examples/index') }}">examples</a>,
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or the <a href="{{ pathto('api/pyplot_summary') }}">list of plotting
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commands</a>.</p>
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<h4>Other learning resources</h4>

‎doc/_templates/layout.html

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</map>
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<div style="background-color: white; text-align: left; padding: 10px 10px 15px 15px">
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<a href="{{ pathto('index') }}"><img src="{{
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pathto("_static/logo2.svg", 1) }}" width="540px" border="0" alt="matplotlib"/></a>
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{%- if builder in ('htmlhelp', 'devhelp', 'latex') %}
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<a href="{{ pathto('index') }}"><img src="{{pathto("_static/logo2.png", 1) }}" width="540px" border="0" alt="matplotlib"/></a>
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{%- else %}
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<a href="{{ pathto('index') }}"><img src="{{pathto("_static/logo2.svg", 1) }}" width="540px" border="0" alt="matplotlib"/></a>
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{%- endif %}
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{% endblock %}

‎doc/faq/installing_faq.rst

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below that goes with your original installation method to cleanly
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remove matplotlib from your system.
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Easy Install
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------------
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1. Delete the caches from your :ref:`.matplotlib configuration directory
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<locating-matplotlib-config-dir>`.
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2. Run::
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easy_install -m matplotlib
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3. Delete any .egg files or directories from your :ref:`installation
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directory <locating-matplotlib-install>`.
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Windows installer
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-----------------
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1. Delete the caches from your :ref:`.matplotlib configuration directory
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<locating-matplotlib-config-dir>`.
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2. Use :menuselection:`Start --> Control Panel` to start the :program:`Add and
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Remove Software` utility.
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Source install
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--------------
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Windows Notes
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=============
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We recommend you use one of the excellent python collections which include
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Python itself and a wide range of libraries including matplotlib:
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- Anaconda_ from `Continuum Analytics`_
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- Canopy_ from Enthought_
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- `Python (x, y) <https://code.google.com/p/pythonxy>`_
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Python (X, Y) is Windows-only, whereas Anaconda and Canopy are cross-platform.
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.. _windows-installers:
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Standalone binary installers for Windows
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----------------------------------------
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If you have already installed Python and numpy, you can use one of the
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matplotlib binary installers for windows -- you can get these from the
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`the PyPI matplotlib page <http://pypi.python.org/pypi/matplotlib>`_
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site. Choose the files with an ``.exe`` extension that match your
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version of Python (e.g., ``py2.7`` if you installed Python 2.7). If
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you haven't already installed Python, you can get the official version
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from the `Python web site <http://python.org/download/>`_.
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See :ref:`installing_windows`.

‎examples/color/named_colors.py

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ncols = 4
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nrows = int(np.ceil(1. * n / ncols))
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fig, ax = plt.subplots()
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fig, ax = plt.subplots(figsize=(8, 5))
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X, Y = fig.get_dpi() * fig.get_size_inches()
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horizontalalignment='left',
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verticalalignment='center')
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# Add extra black line a little bit thicker to make
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# clear colors more visible.
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ax.hlines(
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y, xi_line, xf_line, color='black', linewidth=(h * 0.7))
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ax.hlines(
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y + h * 0.1, xi_line, xf_line, color=colors[name], linewidth=(h * 0.6))
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‎examples/pylab_examples/csd_demo.py

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import numpy as np
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import matplotlib.pyplot as plt
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fig, (ax1, ax2) = plt.subplots(2, 1)
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# make a little extra space between the subplots
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plt.subplots_adjust(wspace=0.5)
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fig.subplots_adjust(hspace=0.5)
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dt = 0.01
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t = np.arange(0, 30, dt)
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s1 = 0.01*np.sin(2*np.pi*10*t) + cnse1
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s2 = 0.01*np.sin(2*np.pi*10*t) + cnse2
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plt.subplot(211)
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plt.plot(t, s1, t, s2)
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plt.xlim(0, 5)
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plt.xlabel('time')
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plt.ylabel('s1 and s2')
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plt.grid(True)
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ax1.plot(t, s1, t, s2)
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ax1.set_xlim(0, 5)
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ax1.set_xlabel('time')
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ax1.set_ylabel('s1 and s2')
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ax1.grid(True)
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plt.subplot(212)
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cxy, f = plt.csd(s1, s2, 256, 1./dt)
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plt.ylabel('CSD (db)')
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cxy, f = ax2.csd(s1, s2, 256, 1./dt)
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ax2.set_ylabel('CSD (db)')
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plt.show()

‎examples/showcase/bachelors_degrees_by_gender.py

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ax.get_xaxis().tick_bottom()
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ax.get_yaxis().tick_left()
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fig.subplots_adjust(left=.06, right=.75, bottom=.02, top=.94)
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# Limit the range of the plot to only where the data is.
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# Avoid unnecessary whitespace.
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plt.xlim(1968.5, 2011.1)
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plt.ylim(-0.25, 90)
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ax.set_xlim(1968.5, 2011.1)
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ax.set_ylim(-0.25, 90)
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ax.get_xaxis().get_major_formatter().set_useOffset(False)
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# Make sure your axis ticks are large enough to be easily read.
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# You don't want your viewers squinting to read your plot.
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# Note that if the title is descriptive enough, it is unnecessary to include
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# axis labels; they are self-evident, in this plot's case.
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plt.title('Percentage of Bachelor\'s degrees conferred to women in '
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'the U.S.A. by major (1970-2011)\n', fontsize=18, ha='center')
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fig.suptitle('Percentage of Bachelor\'s degrees conferred to women in '
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'the U.S.A. by major (1970-2011)\n', fontsize=18, ha='center')
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# Finally, save the figure as a PNG.
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# You can also save it as a PDF, JPEG, etc.
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# Just change the file extension in this call.
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plt.savefig('percent-bachelors-degrees-women-usa.png', bbox_inches='tight')
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# plt.savefig('percent-bachelors-degrees-women-usa.png', bbox_inches='tight')

‎lib/matplotlib/axes/_axes.py

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@docstring.dedent_interpd
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def annotate(self, *args, **kwargs):
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"""
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Create an annotation: a piece of text referring to a data
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point.
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Parameters
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----------
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s : string
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label
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xy : (x, y)
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position of element to annotate. See *xycoords* to control what
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coordinate system this value is interpretated in.
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xytext : (x, y) , optional, default: None
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position of the label `s`. See *textcoords* to control what
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coordinate system this value is interpreted in.
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xycoords : string, optional, default: "data"
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string that indicates what type of coordinates `xy` is. Examples:
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"figure points", "figure pixels", "figure fraction", "axes
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points", .... See `matplotlib.text.Annotation` for more details.
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textcoords : string, optional, default: None
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string that indicates what type of coordinates `text` is. Examples:
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"figure points", "figure pixels", "figure fraction", "axes
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points", .... See `matplotlib.text.Annotation` for more details.
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arrowprops : `matplotlib.lines.Line2D` properties, optional
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Dictionary of line properties for the arrow that connects
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the annotation to the point. If the dictionnary has a key
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`arrowstyle`, a `~matplotlib.patches.FancyArrowPatch`
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instance is created and drawn. See
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`matplotlib.text.Annotation` for more details on valid
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options. Default is None.
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Returns
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-------
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a : `~matplotlib.text.Annotation`
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Notes
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-----
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%(Annotation)s
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Examples
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--------
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.. plot:: mpl_examples/pylab_examples/annotation_demo2.py
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"""
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a = mtext.Annotation(*args, **kwargs)
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a.set_transform(mtransforms.IdentityTransform())
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if 'clip_on' in kwargs:
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a.set_clip_path(self.patch)
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self._add_text(a)
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return a
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annotate.__doc__ = mtext.Annotation.__init__.__doc__
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#### Lines and spans
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@docstring.dedent_interpd

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