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Fix some link redirects in docs
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INSTALL.rst

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Matplotlib requires the following dependencies:
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* `Python <https://www.python.org/downloads/>`_ (>= 3.6)
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* `NumPy <http://www.numpy.org>`_ (>= 1.15)
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* `NumPy <https://numpy.org>`_ (>= 1.15)
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* `setuptools <https://setuptools.readthedocs.io/en/latest/>`_
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* `cycler <http://matplotlib.org/cycler/>`_ (>= 0.10.0)
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* `cycler <https://matplotlib.org/cycler/>`_ (>= 0.10.0)
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* `dateutil <https://pypi.org/project/python-dateutil>`_ (>= 2.1)
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* `kiwisolver <https://github.com/nucleic/kiwi>`_ (>= 1.0.0)
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* `Pillow <https://pillow.readthedocs.io/en/latest/>`_ (>= 6.2)

README.rst

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.. _Downloads: https://pepy.tech/project/matplotlib/month
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.. |NUMFocus| image:: https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A
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.. _NUMFocus: https://www.numfocus.org
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.. _NUMFocus: https://numfocus.org
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.. image:: https://matplotlib.org/_static/logo2.svg
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doc/api/toolkits/mplot3d/faq.rst

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mplot3d FAQ
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***********
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How is mplot3d different from MayaVi?
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How is mplot3d different from Mayavi?
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=====================================
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`MayaVi2 <http://code.enthought.com/projects/mayavi/documentation.php>`_
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`Mayavi <https://docs.enthought.com/mayavi/mayavi/>`_
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is a very powerful and featureful 3D graphing library. For advanced
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3D scenes and excellent rendering capabilities, it is highly recommended to
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use MayaVi2.
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use Mayavi.
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mplot3d was intended to allow users to create simple 3D graphs with the same
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"look-and-feel" as matplotlib's 2D plots. Furthermore, users can use the same
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This problem will likely not be solved until OpenGL support is added to all of
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the backends (patches are greatly welcomed). Until then, if you need complex
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3D scenes, we recommend using
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`MayaVi <http://code.enthought.com/projects/mayavi/documentation.php>`_.
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`MayaVi <https://docs.enthought.com/mayavi/mayavi/>`_.
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I don't like how the 3D plot is laid out, how do I change that?

doc/devel/MEP/MEP11.rst

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For installing from source, and assuming the user has all of the
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C-level compilers and dependencies, this can be accomplished fairly
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easily using distribute_ and following the instructions `here
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<https://pypi.python.org/pypi/distribute>`_. The only anticipated
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<https://pypi.org/project/distribute>`_. The only anticipated
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change to the matplotlib library code will be to import pyparsing_
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from the top-level namespace rather than from within matplotlib. Note
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that distribute_ will also allow us to remove the direct dependency

doc/devel/MEP/MEP12.rst

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* PEP8_ clean-ups (running `flake8
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<https://pypi.python.org/pypi/flake8>`_, or a similar checker, is
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<https://pypi.org/project/flake8>`_, or a similar checker, is
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highly recommended)
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* Commented-out code should be removed.
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* Replace uses of `pylab` interface with `.pyplot` (+ `numpy`,
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references to that example. For example, the API documentation for
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:file:`axes.py` and :file:`pyplot.py` may use these examples to generate
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plots. Use your favorite search tool (e.g., grep, ack, `grin
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<https://pypi.python.org/pypi/grin>`_, `pss
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<https://pypi.python.org/pypi/pss>`_) to search the matplotlib
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<https://pypi.org/project/grin>`_, `pss
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<https://pypi.org/project/pss>`_) to search the matplotlib
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package. See `2dc9a46
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<https://github.com/tonysyu/matplotlib/commit/2dc9a4651e5e566afc0866c603aa8d06aaf32b71>`_
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and `aa6b410

doc/devel/release_guide.rst

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In addition, announcements should be made on social networks (twitter
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via the ``@matplotlib`` account, any other via personal accounts).
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`NumFOCUS <https://www.numfocus.org/>`__ should be contacted for
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`NumFOCUS <https://numfocus.org/>`__ should be contacted for
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inclusion in their newsletter.

doc/index.rst

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.. raw:: html
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<a href="https://www.numfocus.org/">
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<a href="https://numfocus.org/">
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<img src="_static/numfocus_badge.png"
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alt="A Fiscally Sponsored Project of NUMFocus"
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style="float:right; margin-left:20px" />
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<jdh-fellowship_>`_.
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.. _donating: https://numfocus.org/donate-to-matplotlib
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.. _jdh-fellowship: https://www.numfocus.org/programs/john-hunter-technology-fellowship/
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.. _jdh-fellowship: https://numfocus.org/programs/john-hunter-technology-fellowship/
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.. _nf: https://numfocus.org
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The :doc:`Matplotlib license <users/license>` is based on the `Python Software

doc/thirdpartypackages/index.rst

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Basemap
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=======
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`Basemap <http://matplotlib.org/basemap>`_ plots data on map projections, with
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continental and political boundaries.
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`Basemap <https://matplotlib.org/basemap/>`_ plots data on map projections,
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with continental and political boundaries.
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.. image:: /_static/basemap_contour1.png
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:height: 400px
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Cartopy
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=======
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`Cartopy <http://scitools.org.uk/cartopy/docs/latest>`_ builds on top
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`Cartopy <https://scitools.org.uk/cartopy/docs/latest>`_ builds on top
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of Matplotlib to provide object oriented map projection definitions
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and close integration with Shapely for powerful yet easy-to-use vector
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data processing tools. An example plot from the `Cartopy gallery
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<http://scitools.org.uk/cartopy/docs/latest/gallery.html>`_:
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<https://scitools.org.uk/cartopy/docs/latest/gallery.html>`_:
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.. image:: /_static/cartopy_hurricane_katrina_01_00.png
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:height: 400px
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holoviews
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=========
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`holoviews <http://holoviews.org>`_ makes it easier to visualize data
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interactively, especially in a `Jupyter notebook <http://jupyter.org>`_, by
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interactively, especially in a `Jupyter notebook <https://jupyter.org>`_, by
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providing a set of declarative plotting objects that store your data and
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associated metadata. Your data is then immediately visualizable alongside or
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overlaid with other data, either statically or with automatically provided
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mpl-probscale
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=============
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`mpl-probscale <http://matplotlib.org/mpl-probscale/>`_ is a small extension
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`mpl-probscale <https://matplotlib.org/mpl-probscale/>`_ is a small extension
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that allows Matplotlib users to specify probabilty scales. Simply importing the
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``probscale`` module registers the scale with Matplotlib, making it accessible
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via e.g., ``ax.set_xscale('prob')`` or ``plt.yscale('prob')``.

doc/users/history.rst

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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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makes heavy use of `NumPy <https://numpy.org>`_ and other extension
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code to provide good performance even for large arrays.
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Matplotlib is designed with the philosophy that you should be able to

doc/users/prev_whats_new/whats_new_1.0.rst

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The matplotlib trunk is probably in as good a shape as it has ever
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been, thanks to improved `software carpentry
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<http://software-carpentry.org/>`__. We now have a `buildbot
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<https://software-carpentry.org/>`__. We now have a `buildbot
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<https://buildbot.net>`__ which runs a suite of `nose
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<http://code.google.com/p/python-nose/>`__ regression tests on every
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svn commit, auto-generating a set of images and comparing them against

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