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‎INSTALL.rst

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==================
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Installation Guide
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==================
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############
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Installation
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############
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.. note::
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.. toctree::
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:hidden:
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If you wish to contribute to the project, it's recommended you
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:ref:`install the latest development version<install_from_source>`.
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installing_source.rst
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.. contents::
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==============================
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Installing an official release
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==============================
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Matplotlib and its dependencies are available as wheel packages for macOS,
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Windows and Linux distributions::
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Matplotlib releases are available as wheel packages for macOS, Windows and
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Linux on `PyPI <https://pypi.org/project/matplotlib/>`_. Install it using
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``pip``:
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.. code-block:: sh
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python -m pip install -U pip
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python -m pip install -U matplotlib
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animations and a larger selection of file formats, you can
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install :ref:`optional_dependencies`.
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Although not required, we suggest also installing ``IPython`` for
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interactive use. To easily install a complete Scientific Python
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stack, see :ref:`install_scipy_dists` below.
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Third-party distributions of Matplotlib
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=======================================
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.. _install_scipy_dists:
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Scientific Python Distributions
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-------------------------------
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`Anaconda <https://www.anaconda.com/>`_ and `ActiveState
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<https://www.activestate.com/activepython/downloads>`_ are excellent
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choices that "just work" out of the box for Windows, macOS and common
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Linux platforms. `WinPython <https://winpython.github.io/>`_ is an
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option for Windows users. All of these distributions include
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Matplotlib and *lots* of other useful (data) science tools.
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Linux: using your package manager
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---------------------------------
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=========================
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Third-party distributions
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=========================
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If you are on Linux, you might prefer to use your package manager. Matplotlib
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is packaged for almost every major Linux distribution.
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Various third-parties provide Matplotlib for their environments.
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* Debian / Ubuntu: ``sudo apt-get install python3-matplotlib``
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* Fedora: ``sudo dnf install python3-matplotlib``
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* Red Hat: ``sudo yum install python3-matplotlib``
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* Arch: ``sudo pacman -S python-matplotlib``
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.. _install_from_source:
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Installing from source
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======================
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Conda packages
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==============
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Matplotlib is available both via the *anaconda main channel*
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If you are interested in contributing to Matplotlib development,
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running the latest source code, or just like to build everything
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yourself, it is not difficult to build Matplotlib from source.
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.. code-block:: sh
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First you need to install the :ref:`dependencies`.
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conda install matplotlib
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A C compiler is required. Typically, on Linux, you will need ``gcc``, which
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should be installed using your distribution's package manager; on macOS, you
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will need xcode_; on Windows, you will need Visual Studio 2015 or later.
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as well as via the *conda-forge community channel*
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.. _xcode: https://guide.macports.org/chunked/installing.html#installing.xcode
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.. code-block:: sh
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The easiest way to get the latest development version to start contributing
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is to go to the git `repository <https://github.com/matplotlib/matplotlib>`_
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and run::
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conda install -c conda-forge matplotlib
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git clone https://github.com/matplotlib/matplotlib.git
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or::
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Python distributions
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====================
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git clone [email protected]:matplotlib/matplotlib.git
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Matplotlib is part of major Python distributions:
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If you're developing, it's better to do it in editable mode. The reason why
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is that pytest's test discovery only works for Matplotlib
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if installation is done this way. Also, editable mode allows your code changes
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to be instantly propagated to your library code without reinstalling (though
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you will have to restart your python process / kernel)::
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- `Anaconda <https://www.anaconda.com/>`_
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cd matplotlib
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python -m pip install -e .
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- `ActiveState ActivePython
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<https://www.activestate.com/activepython/downloads>`_
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If you're not developing, it can be installed from the source directory with
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a simple (just replace the last step)::
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- `WinPython <https://winpython.github.io/>`_
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python -m pip install .
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Linux package manager
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=====================
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To run the tests you will need to install some additional dependencies::
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If you are using the Python version that comes with your Linux distribution,
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you can install Matplotlib via your package manager, e.g.:
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python -m pip install -r requirements/dev/dev-requirements.txt
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Then, if you want to update your Matplotlib at any time, just do::
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git pull
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When you run ``git pull``, if the output shows that only Python files have
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been updated, you are all set. If C files have changed, you need to run ``pip
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install -e .`` again to compile them.
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There is more information on :ref:`using git <using-git>` in the developer
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docs.
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.. warning::
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The following instructions in this section are for very custom
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installations of Matplotlib. Proceed with caution because these instructions
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may result in your build producing unexpected behavior and/or causing
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local testing to fail.
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If you would like to build from a tarball, grab the latest *tar.gz* release
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file from `the PyPI files page <https://pypi.org/project/matplotlib/>`_.
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We provide a `setup.cfg`_ file which you can use to customize the build
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process. For example, which default backend to use, whether some of the
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optional libraries that Matplotlib ships with are installed, and so on. This
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file will be particularly useful to those packaging Matplotlib.
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.. _setup.cfg: https://raw.githubusercontent.com/matplotlib/matplotlib/master/setup.cfg.template
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* Debian / Ubuntu: ``sudo apt-get install python3-matplotlib``
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* Fedora: ``sudo dnf install python3-matplotlib``
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* Red Hat: ``sudo yum install python3-matplotlib``
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* Arch: ``sudo pacman -S python-matplotlib``
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Building on Windows
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-------------------
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======================
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Installing from source
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======================
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See :ref:`install_from_source`.
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Compiling Matplotlib (or any other extension module, for that matter) requires
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Visual Studio 2015 or later.
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==========================
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Installing for development
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==========================
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See :ref:`installing_for_devs`.
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If you are building your own Matplotlib wheels (or sdists), note that any DLLs
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that you copy into the source tree will be packaged too.

‎doc/_static/mpl.css

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border-color: #ccc;
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}
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div.seealso {
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div.seealso, div.admonition-references {
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background-color: #EAF1F7;
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border-color: #8EADCC;
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color: #3F5E7F;

‎doc/devel/documenting_mpl.rst

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In this way text, code, and figures are output in a "notebook" style.
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References for sphinx-gallery
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-----------------------------
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The showcased Matplotlib functions should be listed in an admonition at the
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bottom as follows
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.. code-block:: python
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###############################################################################
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#
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# .. admonition:: References
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#
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# The use of the following functions, methods, classes and modules is shown
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# in this example:
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#
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# - `matplotlib.axes.Axes.fill` / `matplotlib.pyplot.fill`
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# - `matplotlib.axes.Axes.axis` / `matplotlib.pyplot.axis`
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This allows sphinx-gallery to place an entry to the example in the
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mini-gallery of the mentioned functions. Whether or not a function is mentioned
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here should be decided depending on if a mini-gallery link prominently helps
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to illustrate that function; e.g. mention ``matplotlib.pyplot.subplots`` only
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in examples that are about laying out subplots, not in every example that uses
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it.
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Functions that exist in ``pyplot`` as well as in Axes or Figure should mention
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both references no matter which one is used in the example code. The ``pyplot``
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reference should always be the second to mention; see the example above.
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Order of examples in the gallery
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--------------------------------
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‎doc/users/installing_source.rst

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.. _install_from_source:
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======================
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Installing from source
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======================
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If you are interested in contributing to Matplotlib development,
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running the latest source code, or just like to build everything
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yourself, it is not difficult to build Matplotlib from source.
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First you need to install the :ref:`dependencies`.
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A C compiler is required. Typically, on Linux, you will need ``gcc``, which
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should be installed using your distribution's package manager; on macOS, you
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will need xcode_; on Windows, you will need Visual Studio 2015 or later.
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.. _xcode: https://guide.macports.org/chunked/installing.html#installing.xcode
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The easiest way to get the latest development version to start contributing
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is to go to the git `repository <https://github.com/matplotlib/matplotlib>`_
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and run::
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git clone https://github.com/matplotlib/matplotlib.git
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or::
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git clone [email protected]:matplotlib/matplotlib.git
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If you're developing, it's better to do it in editable mode. The reason why
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is that pytest's test discovery only works for Matplotlib
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if installation is done this way. Also, editable mode allows your code changes
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to be instantly propagated to your library code without reinstalling (though
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you will have to restart your python process / kernel)::
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cd matplotlib
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python -m pip install -e .
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If you're not developing, it can be installed from the source directory with
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a simple (just replace the last step)::
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python -m pip install .
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To run the tests you will need to install some additional dependencies::
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python -m pip install -r requirements/dev/dev-requirements.txt
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Then, if you want to update your Matplotlib at any time, just do::
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git pull
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When you run ``git pull``, if the output shows that only Python files have
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been updated, you are all set. If C files have changed, you need to run ``pip
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install -e .`` again to compile them.
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There is more information on :ref:`using git <using-git>` in the developer
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docs.
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.. warning::
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The following instructions in this section are for very custom
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installations of Matplotlib. Proceed with caution because these instructions
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may result in your build producing unexpected behavior and/or causing
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local testing to fail.
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If you would like to build from a tarball, grab the latest *tar.gz* release
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file from `the PyPI files page <https://pypi.org/project/matplotlib/>`_.
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We provide a `setup.cfg`_ file which you can use to customize the build
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process. For example, which default backend to use, whether some of the
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optional libraries that Matplotlib ships with are installed, and so on. This
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file will be particularly useful to those packaging Matplotlib.
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.. _setup.cfg: https://raw.githubusercontent.com/matplotlib/matplotlib/master/setup.cfg.template
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If you are building your own Matplotlib wheels (or sdists) on Windows, note
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that any DLLs that you copy into the source tree will be packaged too.

‎examples/lines_bars_and_markers/fill.py

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#############################################################################
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###############################################################################
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#
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# ------------
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# .. admonition:: References
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#
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# References
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# """"""""""
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# The use of the following functions, methods, classes and modules is shown
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# in this example:
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#
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# The use of the following functions, methods, classes and modules is shown
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# in this example:
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import matplotlib
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matplotlib.axes.Axes.fill
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matplotlib.pyplot.fill
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matplotlib.axes.Axes.axis
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matplotlib.pyplot.axis
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# - `matplotlib.axes.Axes.fill` / `matplotlib.pyplot.fill`
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# - `matplotlib.axes.Axes.axis` / `matplotlib.pyplot.axis`

‎lib/mpl_toolkits/axisartist/axis_artist.py

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"""
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axis_artist.py module provides axis-related artists. They are
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The :mod:`.axis_artist` module implements custom artists to draw axis elements
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(axis lines and labels, tick lines and labels, grid lines).
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* axis line
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* tick lines
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* tick labels
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* axis label
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* grid lines
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Axis lines and labels and tick lines and labels are managed by the `AxisArtist`
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class; grid lines are managed by the `GridlinesCollection` class.
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The main artist classes are `AxisArtist` and `GridlinesCollection`. While
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`GridlinesCollection` is responsible for drawing grid lines, `AxisArtist`
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is responsible for all other artists. `AxisArtist` has attributes that are
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associated with each type of artists:
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There is one `AxisArtist` per Axis; it can be accessed through
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the ``axis`` dictionary of the parent Axes (which should be a
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`mpl_toolkits.axislines.Axes`), e.g. ``ax.axis["bottom"]``.
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* line: axis line
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* major_ticks: major tick lines
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* major_ticklabels: major tick labels
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* minor_ticks: minor tick lines
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* minor_ticklabels: minor tick labels
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* label: axis label
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Children of the AxisArtist are accessed as attributes: ``.line`` and ``.label``
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for the axis line and label, ``.major_ticks``, ``.major_ticklabels``,
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``.minor_ticks``, ``.minor_ticklabels`` for the tick lines and labels (e.g.
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``ax.axis["bottom"].line``).
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Typically, the `AxisArtist` associated with an axes will be accessed with the
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*axis* dictionary of the axes, i.e., the `AxisArtist` for the bottom axis is ::
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Children properties (colors, fonts, line widths, etc.) can be set using
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setters, e.g. ::
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ax.axis["bottom"]
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where *ax* is an instance of `mpl_toolkits.axislines.Axes`. Thus,
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``ax.axis["bottom"].line`` is an artist associated with the axis line, and
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``ax.axis["bottom"].major_ticks`` is an artist associated with the major tick
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lines.
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You can change the colors, fonts, line widths, etc. of these artists
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by calling suitable set method. For example, to change the color of the major
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ticks of the bottom axis to red, use ::
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ax.axis["bottom"].major_ticks.set_color("r")
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# Make the major ticks of the bottom axis red.
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ax.axis["bottom"].major_ticks.set_color("red")
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However, things like the locations of ticks, and their ticklabels need to be
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changed from the side of the grid_helper.

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