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Merge pull request #2236 from tonysyu/stylesheets
Add easy style sheet selection
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‎doc/conf.py

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extensions = ['matplotlib.sphinxext.mathmpl', 'sphinxext.math_symbol_table',
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'sphinx.ext.autodoc', 'matplotlib.sphinxext.only_directives',
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'sphinx.ext.doctest', 'sphinx.ext.autosummary',
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'matplotlib.sphinxext.plot_directive', 'sphinx.ext.inheritance_diagram',
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'matplotlib.sphinxext.plot_directive',
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'sphinx.ext.inheritance_diagram',
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'sphinxext.gen_gallery', 'sphinxext.gen_rst',
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'matplotlib.sphinxext.ipython_console_highlighting',
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'sphinxext.github',
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('text_labels_and_annotations', 'Text, labels, and annotations'),
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('ticks_and_spines', 'Ticks and spines'),
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('subplots_axes_and_figures', 'Subplots, axes, and figures'),
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('style_sheets', 'Style sheets'),
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('specialty_plots', 'Specialty plots'),
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('showcase', 'Showcase'),
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('api', 'API'),

‎doc/users/beginner.rst

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:maxdepth: 2
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pyplot_tutorial.rst
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style_sheets.rst
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navigation_toolbar.rst
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index_text.rst
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image_tutorial.rst

‎doc/users/style_sheets.rst

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.. _style-sheets
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***********************************
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Customizing plots with style sheets
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***********************************
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The ``style`` package adds support for easy-to-switch plotting "styles" with
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the same parameters as a matplotlibrc_ file.
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There are a number of pre-defined styles provided by matplotlib. For
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example, there's a pre-defined style called "ggplot", which emulates the
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aesthetics of ggplot_ (a popular plotting package for R_). To use this style,
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just add::
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>>> import matplotlib.pyplot as plt
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>>> plt.style.use('ggplot')
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To list all available styles, use::
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>>> print plt.style.available
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Defining your own style
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=======================
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You can create custom styles and use them by calling ``style.use`` with the
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path or URL to the style sheet. Alternatively, if you add your
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``<style-name>.mplstyle`` file to ``~/.matplotlib/stylelib`` (you may need to
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create this directory), you can reuse your custom style sheet with a call to
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``style.use(<style-name>)``. Note that a custom style sheet in
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``~/.matplotlib/stylelib`` will override a style sheet defined by matplotlib if
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the styles have the same name.
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For example, you might want to create
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``~/.matplotlib/stylelib/presentation.mplstyle`` with the following::
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axes.titlesize : 24
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axes.labelsize : 20
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lines.linewidth : 3
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lines.markersize : 10
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xtick.labelsize : 16
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ytick.labelsize : 16
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Then, when you want to adapt a plot designed for a paper to one that looks
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good in a presentation, you can just add::
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>>> import matplotlib.pyplot as plt
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>>> plt.style.use('presentation')
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Composing styles
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================
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Style sheets are designed to be composed together. So you can have a style
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sheet that customizes colors and a separate style sheet that alters element
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sizes for presentations. These styles can easily be combined by passing
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a list of styles::
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>>> import matplotlib.pyplot as plt
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>>> plt.style.use(['dark_background', 'presentation'])
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Note that styles further to the right will overwrite values that are already
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defined by styles on the right.
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Temporary styling
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=================
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If you only want to use a style for a specific block of code but don't want
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to change the global styling, the style package provides a context manager
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for limiting your changes to a specific scope. To isolate the your styling
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changes, you can write something like the following::
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>>> import numpy as np
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>>> import matplotlib.pyplot as plt
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>>>
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>>> with plt.style.context(('dark_background')):
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>>> plt.plot(np.sin(np.linspace(0, 2*np.pi)), 'r-o')
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>>>
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>>> # Some plotting code with the default style
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>>>
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>>> plt.show()
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.. _matplotlibrc: http://matplotlib.sourceforge.net/users/customizing.html
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.. _ggplot: http://had.co.nz/ggplot/
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.. _R: http://www.r-project.org/

‎doc/users/whats_new.rst

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meaningful. If `False` then the full number will be formatted in all
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conditions.
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``style`` package added
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```````````````````````
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You can now easily switch between different styles using the new ``style``
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package::
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>>> from matplotlib import style
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>>> style.use('dark_background')
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Subsequent plots will use updated colors, sizes, etc. To list all available
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styles, use::
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>>> print style.available
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You can add your own custom ``<style name>.mplstyle`` files to
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``~/.matplotlib/stylelib`` or call ``use`` with a URL pointing to a file with
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``matplotlibrc`` settings.
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*Note that this is an experimental feature*, and the interface may change as
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users test out this new feature.
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.. _whats-new-1-3:
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new in matplotlib-1.3

‎examples/style_sheets/plot_dark_background.py

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"""
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This example demonstrates the "dark_background" style, which uses white for
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elements that are typically black (text, borders, etc). Note, however, that not
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all plot elements default to colors defined by an rc parameter.
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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plt.style.use('dark_background')
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L = 6
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x = np.linspace(0, L)
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ncolors = len(plt.rcParams['axes.color_cycle'])
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shift = np.linspace(0, L, ncolors, endpoint=False)
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for s in shift:
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plt.plot(x, np.sin(x + s), 'o-')
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plt.xlabel('x-axis')
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plt.ylabel('y-axis')
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plt.title('title')
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plt.show()

‎examples/style_sheets/plot_ggplot.py

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"""
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This example demonstrates the "ggplot" style, which adjusts the style to
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emulate ggplot_ (a popular plotting package for R_).
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These settings were shamelessly stolen from [1]_ (with permission).
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.. [1] http://www.huyng.com/posts/sane-color-scheme-for-matplotlib/
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.. _ggplot: http://had.co.nz/ggplot/
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.. _R: http://www.r-project.org/
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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plt.style.use('ggplot')
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fig, axes = plt.subplots(ncols=2, nrows=2)
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ax1, ax2, ax3, ax4 = axes.ravel()
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# scatter plot (Note: `plt.scatter` doesn't use default colors)
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x, y = np.random.normal(size=(2, 200))
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ax1.plot(x, y, 'o')
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# sinusoidal lines with colors from default color cycle
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L = 2*np.pi
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x = np.linspace(0, L)
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ncolors = len(plt.rcParams['axes.color_cycle'])
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shift = np.linspace(0, L, ncolors, endpoint=False)
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for s in shift:
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ax2.plot(x, np.sin(x + s), '-')
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ax2.margins(0)
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# bar graphs
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x = np.arange(5)
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y1, y2 = np.random.randint(1, 25, size=(2, 5))
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width = 0.25
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ax3.bar(x, y1, width)
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ax3.bar(x+width, y2, width, color=plt.rcParams['axes.color_cycle'][2])
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ax3.set_xticks(x+width)
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ax3.set_xticklabels(['a', 'b', 'c', 'd', 'e'])
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# circles with colors from default color cycle
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for i, color in enumerate(plt.rcParams['axes.color_cycle']):
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xy = np.random.normal(size=2)
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ax4.add_patch(plt.Circle(xy, radius=0.3, color=color))
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ax4.axis('equal')
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ax4.margins(0)
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plt.show()

‎examples/style_sheets/plot_grayscale.py

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"""
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This example demonstrates the "grayscale" style sheet, which changes all colors
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that are defined as rc parameters to grayscale. Note, however, that not all
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plot elements default to colors defined by an rc parameter.
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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def color_cycle_example(ax):
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L = 6
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x = np.linspace(0, L)
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ncolors = len(plt.rcParams['axes.color_cycle'])
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shift = np.linspace(0, L, ncolors, endpoint=False)
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for s in shift:
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ax.plot(x, np.sin(x + s), 'o-')
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def image_and_patch_example(ax):
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ax.imshow(np.random.random(size=(20, 20)), interpolation='none')
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c = plt.Circle((5, 5), radius=5, label='patch')
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ax.add_patch(c)
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plt.style.use('grayscale')
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fig, (ax1, ax2) = plt.subplots(ncols=2)
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color_cycle_example(ax1)
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image_and_patch_example(ax2)
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plt.show()

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