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Normalize all 'e.g.' instances. Addresses issue #1423. #1916

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Apr 18, 2013
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Normalize all 'e.g.' instances. Addresses issue #1423. #1916

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3a8d7f0
Normalize all 'e.g.' instances. Addresses issue #1423.
cimarronm Apr 17, 2013
e133ace
Reformatted two longs line in artists.rst.
cimarronm Apr 17, 2013
cbabdb2
Updated modified lines to ensure linewidth <= 79 according to PEP8.
cimarronm Apr 18, 2013
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Reformatted two longs line in artists.rst.
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@cimarronm
cimarronm committed Apr 17, 2013
commit e133ace6b75897a88918a2ea86b7b4e51d7276bd
33 changes: 30 additions & 3 deletions doc/users/artists.rst
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Expand Up @@ -19,8 +19,24 @@ the ``Artist`` handles all the high level constructs like representing
and laying out the figure, text, and lines. The typical user will
spend 95% of his time working with the ``Artists``.

There are two types of ``Artists``: primitives and containers. The primitives represent the standard graphical objects we want to paint onto our canvas: :class:`~matplotlib.lines.Line2D`, :class:`~matplotlib.patches.Rectangle`, :class:`~matplotlib.text.Text`, :class:`~matplotlib.image.AxesImage`, etc., and the containers are places to put them (:class:`~matplotlib.axis.Axis`, :class:`~matplotlib.axes.Axes` and :class:`~matplotlib.figure.Figure`). The standard use is to create a :class:`~matplotlib.figure.Figure` instance, use the ``Figure`` to create one or more :class:`~matplotlib.axes.Axes` or :class:`~matplotlib.axes.Subplot` instances, and use the ``Axes`` instance helper methods to create the primitives. In the example below, we create a ``Figure`` instance using :func:`matplotlib.pyplot.figure`, which is a convenience method for instantiating ``Figure`` instances and connecting them with your user interface or drawing toolkit ``FigureCanvas``. As we will discuss below, this is not necessary -- you
can work directly with PostScript, PDF Gtk+, or wxPython ``FigureCanvas`` instances, instantiate your ``Figures`` directly and connect them yourselves -- but since we are focusing here on the ``Artist`` API we'll let :mod:`~matplotlib.pyplot` handle some of those details for us::
There are two types of ``Artists``: primitives and containers. The primitives
represent the standard graphical objects we want to paint onto our canvas:
:class:`~matplotlib.lines.Line2D`, :class:`~matplotlib.patches.Rectangle`,
:class:`~matplotlib.text.Text`, :class:`~matplotlib.image.AxesImage`, etc., and
the containers are places to put them (:class:`~matplotlib.axis.Axis`,
:class:`~matplotlib.axes.Axes` and :class:`~matplotlib.figure.Figure`). The
standard use is to create a :class:`~matplotlib.figure.Figure` instance, use
the ``Figure`` to create one or more :class:`~matplotlib.axes.Axes` or
:class:`~matplotlib.axes.Subplot` instances, and use the ``Axes`` instance
helper methods to create the primitives. In the example below, we create a
``Figure`` instance using :func:`matplotlib.pyplot.figure`, which is a
convenience method for instantiating ``Figure`` instances and connecting them
with your user interface or drawing toolkit ``FigureCanvas``. As we will
discuss below, this is not necessary -- you can work directly with PostScript,
PDF Gtk+, or wxPython ``FigureCanvas`` instances, instantiate your ``Figures``
directly and connect them yourselves -- but since we are focusing here on the
``Artist`` API we'll let :mod:`~matplotlib.pyplot` handle some of those details
for us::

import matplotlib.pyplot as plt
fig = plt.figure()
Expand Down Expand Up @@ -517,7 +533,18 @@ and zooming, as well as the :class:`~matplotlib.ticker.Locator` and
:class:`~matplotlib.ticker.Formatter` instances which control where
the ticks are placed and how they are represented as strings.

Each ``Axis`` object contains a :attr:`~matplotlib.axis.Axis.label` attribute (this is what :mod:`~matplotlib.pylab` modifies in calls to :func:`~matplotlib.pylab.xlabel` and :func:`~matplotlib.pylab.ylabel`) as well as a list of major and minor ticks. The ticks are :class:`~matplotlib.axis.XTick` and :class:`~matplotlib.axis.YTick` instances, which contain the actual line and text primitives that render the ticks and ticklabels. Because the ticks are dynamically created as needed (e.g., when panning and zooming), you should access the lists of major and minor ticks through their accessor methods :meth:`~matplotlib.axis.Axis.get_major_ticks` and :meth:`~matplotlib.axis.Axis.get_minor_ticks`. Although the ticks contain all the primitives and will be covered below, the ``Axis`` methods contain accessor methods to return the tick lines, tick labels, tick locations etc.:
Each ``Axis`` object contains a :attr:`~matplotlib.axis.Axis.label` attribute
(this is what :mod:`~matplotlib.pylab` modifies in calls to
:func:`~matplotlib.pylab.xlabel` and :func:`~matplotlib.pylab.ylabel`) as well
as a list of major and minor ticks. The ticks are
:class:`~matplotlib.axis.XTick` and :class:`~matplotlib.axis.YTick` instances,
which contain the actual line and text primitives that render the ticks and
ticklabels. Because the ticks are dynamically created as needed (e.g., when
panning and zooming), you should access the lists of major and minor ticks
through their accessor methods :meth:`~matplotlib.axis.Axis.get_major_ticks`
and :meth:`~matplotlib.axis.Axis.get_minor_ticks`. Although the ticks contain
all the primitives and will be covered below, the ``Axis`` methods contain
accessor methods to return the tick lines, tick labels, tick locations etc.:

.. sourcecode:: ipython

Expand Down