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‎.flake8

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lib/mpl_toolkits/axes_grid1/colorbar.py: E225, E501
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lib/mpl_toolkits/axisartist/angle_helper.py: E221
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lib/mpl_toolkits/axisartist/clip_path.py: E225
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lib/mpl_toolkits/axisartist/floating_axes.py: E225, E402, E501
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lib/mpl_toolkits/axisartist/grid_helper_curvelinear.py: E225, E501
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doc/conf.py: E402, E501

‎INSTALL.rst

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@@ -147,14 +147,15 @@ Optionally, you can also install a number of packages to enable better user
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interface toolkits. See :ref:`what-is-a-backend` for more details on the
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optional Matplotlib backends and the capabilities they provide.
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* :term:`tk` (>= 8.3, != 8.6.0 or 8.6.1): for the Tk-based backends;
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* `Tk <https://docs.python.org/3/library/tk.html>`_ (>= 8.3, != 8.6.0 or
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8.6.1): for the Tk-based backends;
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* `PyQt4 <https://pypi.org/project/PyQt4>`_ (>= 4.6) or
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`PySide <https://pypi.org/project/PySide>`_ (>= 1.0.3): for the Qt4-based
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backends;
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* `PyQt5 <https://pypi.org/project/PyQt5>`_: for the Qt5-based backends;
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* `PyGObject <https://pypi.org/project/PyGObject/>`_: for the GTK3-based
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backends;
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* :term:`wxpython` (>= 4): for the WX-based backends;
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* `wxPython <https://www.wxpython.org/>`_ (>= 4): for the WX-based backends;
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* `cairocffi <https://cairocffi.readthedocs.io/en/latest/>`_ (>= 0.8) or
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`pycairo <https://pypi.org/project/pycairo>`_: for the cairo-based
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backends;

‎doc/_static/ridge_map_white_mountains.png

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‎doc/_templates/layout.html

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@@ -76,27 +76,10 @@ <h3>{{ _('Navigation') }}</h3>
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{%- endmacro %}
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{%- macro script() %}
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{% if sphinx_version >= "1.8.0" %}
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<script type="text/javascript" id="documentation_options" data-url_root="{{ pathto('', 1) }}" src="{{ pathto('_static/documentation_options.js', 1) }}"></script>
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{%- for scriptfile in script_files %}
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{{ js_tag(scriptfile) }}
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{%- endfor %}
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{% else %}
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<script type="text/javascript">
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var DOCUMENTATION_OPTIONS = {
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URL_ROOT:'{{ url_root }}',
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VERSION:'{{ release|e }}',
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LANGUAGE:'{{ language }}',
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COLLAPSE_INDEX:false,
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FILE_SUFFIX:'{{ '' if no_search_suffix else file_suffix }}',
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HAS_SOURCE: {{ has_source|lower }},
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SOURCELINK_SUFFIX: '{{ sourcelink_suffix }}'
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};
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</script>
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{%- for scriptfile in script_files %}
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<script type="text/javascript" src="{{ pathto(scriptfile, 1) }}"></script>
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{%- endfor %}
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{% endif %}
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<script type="text/javascript" id="documentation_options" data-url_root="{{ pathto('', 1) }}" src="{{ pathto('_static/documentation_options.js', 1) }}"></script>
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{%- for scriptfile in script_files %}
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{{ js_tag(scriptfile) }}
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{%- endfor %}
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{%- endmacro %}
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{%- macro css() %}

‎doc/api/next_api_changes/2019-03-04-AL.rst

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Autoscaling changes
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```````````````````
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Matplotlib used to recompute autoscaled limits after every plotting
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(``plot()``, ``bar()``, etc.) call. It now only does so when actually
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rendering the canvas, or when the user queries the Axes limits. This is a
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major performance improvement for plots with a large number of artists.
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In particular, this means that artists added manually with `Axes.add_line`,
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`Axes.add_patch`, etc. will be taken into account by the autoscale, even
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without an explicit call to `Axes.autoscale_view`.
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In some cases, this can result in different limits being reported. If this is
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an issue, consider triggering a draw with `fig.canvas.draw`.
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LogLocator.nonsingular now maintains the orders of its arguments
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````````````````````````````````````````````````````````````````
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It no longer reorders them in increasing order. The new behavior is consistent
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with MaxNLocator.

‎doc/api/next_api_changes/2019-04-17-AL.rst

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Change in the application of ``Artist.sticky_edges``
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````````````````````````````````````````````````````
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Previously, the ``sticky_edges`` attribute of artists was a list of values such
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that if an axis limit coincides with a sticky edge, it would not be expanded by
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the axes margins (this is the mechanism that e.g. prevents margins from being
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added around images).
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``sticky_edges`` now have an additional effect on margins application: even if
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an axis limit did not coincide with a sticky edge, it cannot *cross* a sticky
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edge through margin application -- instead, the margins will only expand the
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axis limit until it bumps against the sticky edge.
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This change improves the margins of axes displaying a `~Axes.streamplot`:
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- if the streamplot goes all the way to the edges of the vector field, then the
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axis limits are set to match exactly the vector field limits (whereas they
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would be sometimes be off by a small floating point error previously).
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- if the streamplot does not reach the edges of the vector field (e.g., due to
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the use of ``start_points`` and ``maxlength``), then margins expansion will
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not cross the the vector field limits anymore.
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This change is also used internally to ensure that polar plots don't display
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negative *r* values unless the user really passes in a negative value.

‎doc/api/next_api_changes/2019-05-01-AL.rst

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Deprecations
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````````````
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``FigureCanvasMac.invalidate`` is deprecated in favor of its synonym,
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``FigureCanvasMac.draw_idle``.

‎doc/glossary/index.rst

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Cairo
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The `Cairo graphics <https://cairographics.org>`_ engine
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dateutil
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The `dateutil <https://dateutil.readthedocs.io>`_ library
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provides extensions to the standard datetime module
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EPS
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Encapsulated Postscript (`EPS
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<https://en.wikipedia.org/wiki/Encapsulated_PostScript>`_)
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FreeType
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`FreeType <https://www.freetype.org/>`_ is a font rasterization
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library used by matplotlib which supports TrueType, Type 1, and
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OpenType fonts.
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GDK
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The Gimp Drawing Kit for GTK+
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GTK
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The GIMP Toolkit (`GTK <https://www.gtk.org/>`_) graphical user interface
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library
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JPG
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The Joint Photographic Experts Group (`JPEG
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<https://en.wikipedia.org/wiki/Jpeg>`_) compression method and
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file format for photographic images
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numpy
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`numpy <http://www.numpy.org/>`_ is the standard numerical
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array library for python, the successor to Numeric and numarray.
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numpy provides fast operations for homogeneous data sets and
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common mathematical operations like correlations, standard
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deviation, fourier transforms, and convolutions.
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PDF
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Adobe's Portable Document Format (`PDF
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<https://en.wikipedia.org/wiki/Portable_Document_Format>`_)
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PNG
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Portable Network Graphics (`PNG
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<https://en.wikipedia.org/wiki/Portable_Network_Graphics>`_), a raster
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graphics format that employs lossless data compression which is more
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suitable for line art than the lossy jpg format. Unlike the gif format,
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png is not encumbered by requirements for a patent license.
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PS
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Postscript (`PS <https://en.wikipedia.org/wiki/PostScript>`_) is a
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vector graphics ASCII text language widely used in printers and
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publishing. Postscript was developed by adobe systems and is
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starting to show its age: for example is does not have an alpha
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channel. PDF was designed in part as a next-generation document
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format to replace postscript
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PyGObject
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`PyGObject <http://www.pygtk.org/>`_ provides Python wrappers for the
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:term:`GTK` widgets library
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and windows; many linux distributions package this as
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'python-qt5' or 'python-qt4'.
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python
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`python <https://www.python.org>`_ is an object oriented interpreted
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language widely used for scripting, application development, web
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application servers, scientific computing and more.
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Qt
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`Qt <https://www.qt.io/>`__ is a cross-platform
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application framework for desktop and embedded development.
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version of Qt cross-platform application
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framework for desktop and embedded development.
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raster graphics
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`Raster graphics
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<https://en.wikipedia.org/wiki/Raster_graphics>`_, or bitmaps,
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represent an image as an array of pixels which is resolution
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dependent. Raster graphics are generally most practical for
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photo-realistic images, but do not scale easily without loss of
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quality.
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SVG
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The Scalable Vector Graphics format (`SVG
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<https://en.wikipedia.org/wiki/Svg>`_). An XML based vector
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graphics format supported by many web browsers.
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TIFF
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Tagged Image File Format (`TIFF
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<https://en.wikipedia.org/wiki/Tagged_Image_File_Format>`_) is a
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file format for storing images, including photographs and line
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art.
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Tk
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`Tk <http://www.tcl.tk/>`_ is a graphical user interface for Tcl
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and many other dynamic languages. It can produce rich, native
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applications that run unchanged across Windows, Mac OS X, Linux
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and more.
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vector graphics
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`vector graphics
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<https://en.wikipedia.org/wiki/Vector_graphics>`_ use geometrical
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primitives based upon mathematical equations to represent images
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in computer graphics. Primitives can include points, lines,
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curves, and shapes or polygons. Vector graphics are scalable,
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which means that they can be resized without suffering from
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issues related to inherent resolution like are seen in raster
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graphics. Vector graphics are generally most practical for
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typesetting and graphic design applications.
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`wxpython <https://www.wxpython.org/>`_ provides python wrappers
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for the :term:`wxWidgets` library for use with the WX and WXAgg

‎doc/thirdpartypackages/index.rst

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.. image:: /_static/cartopy_hurricane_katrina_01_00.png
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:height: 400px
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Ridge Map
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=========
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`ridge_map <https://github.com/ColCarroll/ridge_map>`_ uses Matplotlib,
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SRTM.py, NumPy, and scikit-image to make ridge plots of your favorite
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ridges.
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.. image:: /_static/ridge_map_white_mountains.png
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:height: 364px
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Declarative libraries
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*********************
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‎doc/users/next_whats_new/2019-04-05-AL.rst

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`~pyplot.imsave` gained support for the ``metadata`` and ``pil_kwargs`` parameters
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``````````````````````````````````````````````````````````````````````````````````
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These parameters behave similarly as for the `Figure.savefig()` method.

‎examples/lines_bars_and_markers/gradient_bar.py

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"""
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============
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Gradient Bar
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============
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========================
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Bar chart with gradients
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========================
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Matplotlib does not natively support gradients. However, we can emulate a
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gradient-filled rectangle by an `.AxesImage` of the right size and coloring.
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In particular, we use a colormap to generate the actual colors. It is then
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sufficient to define the underlying values on the corners of the image and
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let bicubic interpolation fill out the area. We define the gradient direction
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by a unit vector *v*. The values at the corners are then obtained by the
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lengths of the projections of the corner vectors on *v*.
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A similar approach can be used to create a gradient background for an axes.
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In that case, it is helpful to uses Axes coordinates
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(`extent=(0, 1, 0, 1), transform=ax.transAxes`) to be independent of the data
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coordinates.
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np.random.seed(19680801)
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def gbar(ax, x, y, width=0.5, bottom=0):
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X = [[.6, .6], [.7, .7]]
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def gradient_image(ax, extent, direction=0.3, cmap_range=(0, 1), **kwargs):
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"""
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Draw a gradient image based on a colormap.
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Parameters
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----------
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ax : Axes
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The axes to draw on.
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extent
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The extent of the image as (xmin, xmax, ymin, ymax).
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By default, this is in Axes coordinates but may be
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changed using the *transform* kwarg.
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direction : float
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The direction of the gradient. This is a number in
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range 0 (=vertical) to 1 (=horizontal).
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cmap_range : float, float
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The fraction (cmin, cmax) of the colormap that should be
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used for the gradient, where the complete colormap is (0, 1).
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**kwargs
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Other parameters are passed on to `.Axes.imshow()`.
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In particular useful is *cmap*.
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"""
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phi = direction * np.pi / 2
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v = np.array([np.cos(phi), np.sin(phi)])
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X = np.array([[v @ [1, 0], v @ [1, 1]],
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[v @ [0, 0], v @ [0, 1]]])
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a, b = cmap_range
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X = a + (b - a) / X.max() * X
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im = ax.imshow(X, extent=extent, interpolation='bicubic',
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vmin=0, vmax=1, **kwargs)
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return im
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def gradient_bar(ax, x, y, width=0.5, bottom=0):
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for left, top in zip(x, y):
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ax.imshow(X, interpolation='bicubic', cmap=plt.cm.Blues,
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extent=(left, right, bottom, top), alpha=1)
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gradient_image(ax, extent=(left, right, bottom, top),
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cmap=plt.cm.Blues_r, cmap_range=(0, 0.8))
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xmin, xmax = xlim = 0, 10
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X = [[.6, .6], [.7, .7]]
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ax.imshow(X, interpolation='bicubic', cmap=plt.cm.copper,
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extent=(xmin, xmax, ymin, ymax), alpha=1)
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# background image
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gradient_image(ax, direction=0, extent=(0, 1, 0, 1), transform=ax.transAxes,
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cmap=plt.cm.Oranges, cmap_range=(0.1, 0.6))
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N = 10
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x = np.arange(N) + 0.25
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x = np.arange(N) + 0.15
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y = np.random.rand(N)
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gbar(ax, x, y, width=0.7)
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gradient_bar(ax, x, y, width=0.7)
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ax.set_aspect('auto')
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plt.show()

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