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===============
Artist tutorial
===============
Using Artist objects to render on the canvas.
There are three layers to the Matplotlib API.
* the :class:`matplotlib.backend_bases.FigureCanvas` is the area onto which
  the figure is drawn
* the :class:`matplotlib.backend_bases.Renderer` is the object which knows how
  to draw on the :class:`~matplotlib.backend_bases.FigureCanvas`
* and the :class:`matplotlib.artist.Artist` is the object that knows how to use
  a renderer to paint onto the canvas.
The :class:`~matplotlib.backend_bases.FigureCanvas` and
:class:`~matplotlib.backend_bases.Renderer` handle all the details of
talking to user interface toolkits like `wxPython
<https://www.wxpython.org>`_ or drawing languages like PostScript®, and
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 their 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
    import matplotlib.pyplot as plt
    fig = plt.figure()
    ax = fig.add_subplot(2, 1, 1) # two rows, one column, first plot
The :class:`~matplotlib.axes.Axes` is probably the most important
class in the Matplotlib API, and the one you will be working with most
of the time.  This is because the ``Axes`` is the plotting area into
which most of the objects go, and the ``Axes`` has many special helper
methods (:meth:`~matplotlib.axes.Axes.plot`,
:meth:`~matplotlib.axes.Axes.text`,
:meth:`~matplotlib.axes.Axes.hist`,
:meth:`~matplotlib.axes.Axes.imshow`) to create the most common
graphics primitives (:class:`~matplotlib.lines.Line2D`,
:class:`~matplotlib.text.Text`,
:class:`~matplotlib.patches.Rectangle`,
:class:`~matplotlib.image.AxesImage`, respectively).  These helper methods
will take your data (e.g., ``numpy`` arrays and strings) and create
primitive ``Artist`` instances as needed (e.g., ``Line2D``), add them to
the relevant containers, and draw them when requested.  Most of you
are probably familiar with the :class:`~matplotlib.axes.Subplot`,
which is just a special case of an ``Axes`` that lives on a regular
rows by columns grid of ``Subplot`` instances.  If you want to create
an ``Axes`` at an arbitrary location, simply use the
:meth:`~matplotlib.figure.Figure.add_axes` method which takes a list
of ``[left, bottom, width, height]`` values in 0-1 relative figure
coordinates::
    fig2 = plt.figure()
    ax2 = fig2.add_axes([0.15, 0.1, 0.7, 0.3])
Continuing with our example::
    import numpy as np
    t = np.arange(0.0, 1.0, 0.01)
    s = np.sin(2*np.pi*t)
    line, = ax.plot(t, s, color='blue', lw=2)
In this example, ``ax`` is the ``Axes`` instance created by the
``fig.add_subplot`` call above (remember ``Subplot`` is just a subclass of
``Axes``) and when you call ``ax.plot``, it creates a ``Line2D`` instance and
adds it to the ``Axes``.  In the interactive `IPython <https://ipython.org/>`_
session below, you can see that the ``Axes.lines`` list is length one and
contains the same line that was returned by the ``line, = ax.plot...`` call:
.. sourcecode:: ipython
    In [101]: ax.lines[0]
    Out[101]: <matplotlib.lines.Line2D at 0x19a95710>
    In [102]: line
    Out[102]: <matplotlib.lines.Line2D at 0x19a95710>
If you make subsequent calls to ``ax.plot`` (and the hold state is "on"
which is the default) then additional lines will be added to the list.
You can remove a line later by calling its ``remove`` method::
    line = ax.lines[0]
    line.remove()
The Axes also has helper methods to configure and decorate the x-axis
and y-axis tick, tick labels and axis labels::
    xtext = ax.set_xlabel('my xdata')  # returns a Text instance
    ytext = ax.set_ylabel('my ydata')
When you call :meth:`ax.set_xlabel <matplotlib.axes.Axes.set_xlabel>`,
it passes the information on the :class:`~matplotlib.text.Text`
instance of the :class:`~matplotlib.axis.XAxis`.  Each ``Axes``
instance contains an :class:`~matplotlib.axis.XAxis` and a
:class:`~matplotlib.axis.YAxis` instance, which handle the layout and
drawing of the ticks, tick labels and axis labels.
Try creating the figure below.
# sphinx_gallery_capture_repr = ('__repr__',)
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
fig.subplots_adjust(top=0.8)
ax1 = fig.add_subplot(211)
ax1.set_ylabel('Voltage [V]')
ax1.set_title('A sine wave')
t = np.arange(0.0, 1.0, 0.01)
s = np.sin(2*np.pi*t)
line, = ax1.plot(t, s, color='blue', lw=2)
# Fixing random state for reproducibility
np.random.seed(19680801)
ax2 = fig.add_axes([0.15, 0.1, 0.7, 0.3])
n, bins, patches = ax2.hist(np.random.randn(1000), 50,
                            facecolor='yellow', edgecolor='yellow')
ax2.set_xlabel('Time [s]')
###############################################################################
# .. _customizing-artists:
# Customizing your objects
# ========================
# Every element in the figure is represented by a Matplotlib
# :class:`~matplotlib.artist.Artist`, and each has an extensive list of
# properties to configure its appearance.  The figure itself contains a
# :class:`~matplotlib.patches.Rectangle` exactly the size of the figure,
# which you can use to set the background color and transparency of the
# figures.  Likewise, each :class:`~matplotlib.axes.Axes` bounding box
# (the standard white box with black edges in the typical Matplotlib
# plot, has a ``Rectangle`` instance that determines the color,
# transparency, and other properties of the Axes.  These instances are
# stored as member variables :attr:`Figure.patch
# <matplotlib.figure.Figure.patch>` and :attr:`Axes.patch
# <matplotlib.axes.Axes.patch>` ("Patch" is a name inherited from
# MATLAB, and is a 2D "patch" of color on the figure, e.g., rectangles,
# circles and polygons).  Every Matplotlib ``Artist`` has the following
# properties
# ==========  =================================================================
# Property    Description
# ==========  =================================================================
# alpha       The transparency - a scalar from 0-1
# animated    A boolean that is used to facilitate animated drawing
# axes        The Axes that the Artist lives in, possibly None
# clip_box    The bounding box that clips the Artist
# clip_on     Whether clipping is enabled
# clip_path   The path the artist is clipped to
# contains    A picking function to test whether the artist contains the pick
#             point
# figure      The figure instance the artist lives in, possibly None
# label       A text label (e.g., for auto-labeling)
# picker      A python object that controls object picking
# transform   The transformation
# visible     A boolean whether the artist should be drawn
# zorder      A number which determines the drawing order
# rasterized  Boolean; Turns vectors into raster graphics (for compression &
#             EPS transparency)
# ==========  =================================================================
# Each of the properties is accessed with an old-fashioned setter or
# getter (yes we know this irritates Pythonistas and we plan to support
# direct access via properties or traits but it hasn't been done yet).
# For example, to multiply the current alpha by a half::
#     a = o.get_alpha()
#     o.set_alpha(0.5*a)
# If you want to set a number of properties at once, you can also use
# the ``set`` method with keyword arguments.  For example::
#     o.set(alpha=0.5, zorder=2)
# If you are working interactively at the python shell, a handy way to
# inspect the ``Artist`` properties is to use the
# :func:`matplotlib.artist.getp` function (simply
# :func:`~matplotlib.pyplot.getp` in pyplot), which lists the properties
# and their values.  This works for classes derived from ``Artist`` as
# well, e.g., ``Figure`` and ``Rectangle``.  Here are the ``Figure`` rectangle
# properties mentioned above:
# .. sourcecode:: ipython
#     In [149]: matplotlib.artist.getp(fig.patch)
#       agg_filter = None
#       alpha = None
#       animated = False
#       antialiased or aa = False
#       bbox = Bbox(x0=0.0, y0=0.0, x1=1.0, y1=1.0)
#       capstyle = butt
#       children = []
#       clip_box = None
#       clip_on = True
#       clip_path = None
#       contains = None
#       data_transform = BboxTransformTo(     TransformedBbox(         Bbox...
#       edgecolor or ec = (1.0, 1.0, 1.0, 1.0)
#       extents = Bbox(x0=0.0, y0=0.0, x1=640.0, y1=480.0)
#       facecolor or fc = (1.0, 1.0, 1.0, 1.0)
#       figure = Figure(640x480)
#       fill = True
#       gid = None
#       hatch = None
#       height = 1
#       in_layout = False
#       joinstyle = miter
#       label =
#       linestyle or ls = solid
#       linewidth or lw = 0.0
#       patch_transform = CompositeGenericTransform(     BboxTransformTo(   ...
#       path = Path(array([[0., 0.],        [1., 0.],        [1.,...
#       path_effects = []
#       picker = None
#       rasterized = None
#       sketch_params = None
#       snap = None
#       transform = CompositeGenericTransform(     CompositeGenericTra...
#       transformed_clip_path_and_affine = (None, None)
#       url = None
#       verts = [[  0.   0.]  [640.   0.]  [640. 480.]  [  0. 480....
#       visible = True
#       width = 1
#       window_extent = Bbox(x0=0.0, y0=0.0, x1=640.0, y1=480.0)
#       x = 0
#       xy = (0, 0)
#       y = 0
#       zorder = 1
# The docstrings for all of the classes also contain the ``Artist``
# properties, so you can consult the interactive "help" or the
# :ref:`artist-api` for a listing of properties for a given object.
# .. _object-containers:
# Object containers
# =================
# Now that we know how to inspect and set the properties of a given
# object we want to configure, we need to know how to get at that object.
# As mentioned in the introduction, there are two kinds of objects:
# primitives and containers.  The primitives are usually the things you
# want to configure (the font of a :class:`~matplotlib.text.Text`
# instance, the width of a :class:`~matplotlib.lines.Line2D`) although
# the containers also have some properties as well -- for example the
# :class:`~matplotlib.axes.Axes` :class:`~matplotlib.artist.Artist` is a
# container that contains many of the primitives in your plot, but it
# also has properties like the ``xscale`` to control whether the xaxis
# is 'linear' or 'log'.  In this section we'll review where the various
# container objects store the ``Artists`` that you want to get at.
# .. _figure-container:
# Figure container
# ----------------
# The top level container ``Artist`` is the
# :class:`matplotlib.figure.Figure`, and it contains everything in the
# figure.  The background of the figure is a
# :class:`~matplotlib.patches.Rectangle` which is stored in
# :attr:`Figure.patch <matplotlib.figure.Figure.patch>`.  As
# you add subplots (:meth:`~matplotlib.figure.Figure.add_subplot`) and
# axes (:meth:`~matplotlib.figure.Figure.add_axes`) to the figure
# these will be appended to the :attr:`Figure.axes
# <matplotlib.figure.Figure.axes>`.  These are also returned by the
# methods that create them:
# .. sourcecode:: ipython
#     In [156]: fig = plt.figure()
#     In [157]: ax1 = fig.add_subplot(211)
#     In [158]: ax2 = fig.add_axes([0.1, 0.1, 0.7, 0.3])
#     In [159]: ax1
#     Out[159]: <AxesSubplot:>
#     In [160]: print(fig.axes)
#     [<AxesSubplot:>, <matplotlib.axes._axes.Axes object at 0x7f0768702be0>]
# Because the figure maintains the concept of the "current Axes" (see
# :meth:`Figure.gca <matplotlib.figure.Figure.gca>` and
# :meth:`Figure.sca <matplotlib.figure.Figure.sca>`) to support the
# pylab/pyplot state machine, you should not insert or remove Axes
# directly from the Axes list, but rather use the
# :meth:`~matplotlib.figure.Figure.add_subplot` and
# :meth:`~matplotlib.figure.Figure.add_axes` methods to insert, and the
# `Axes.remove <matplotlib.artist.Artist.remove>` method to delete.  You are
# free however, to iterate over the list of Axes or index into it to get
# access to ``Axes`` instances you want to customize.  Here is an
# example which turns all the Axes grids on::
#     for ax in fig.axes:
#         ax.grid(True)
# The figure also has its own ``images``, ``lines``, ``patches`` and ``text``
# attributes, which you can use to add primitives directly. When doing so, the
# default coordinate system for the ``Figure`` will simply be in pixels (which
# is not usually what you want). If you instead use Figure-level methods to add
# Artists (e.g., using `.Figure.text` to add text), then the default coordinate
# system will be "figure coordinates" where (0, 0) is the bottom-left of the
# figure and (1, 1) is the top-right of the figure.
# As with all ``Artist``\s, you can control this coordinate system by setting
# the transform property. You can explicitly use "figure coordinates" by
# setting the ``Artist`` transform to :attr:`fig.transFigure
# <matplotlib.figure.Figure.transFigure>`:
import matplotlib.lines as lines
fig = plt.figure()
l1 = lines.Line2D([0, 1], [0, 1], transform=fig.transFigure, figure=fig)
l2 = lines.Line2D([0, 1], [1, 0], transform=fig.transFigure, figure=fig)
fig.lines.extend([l1, l2])
###############################################################################
# Here is a summary of the Artists the Figure contains
# ================ ============================================================
# Figure attribute Description
# ================ ============================================================
# axes             A list of `~.axes.Axes` instances (includes Subplot)
# patch            The `.Rectangle` background
# images           A list of `.FigureImage` patches -
#                  useful for raw pixel display
# legends          A list of Figure `.Legend` instances
#                  (different from ``Axes.get_legend()``)
# lines            A list of Figure `.Line2D` instances
#                  (rarely used, see ``Axes.lines``)
# patches          A list of Figure `.Patch`\s
#                  (rarely used, see ``Axes.patches``)
# texts            A list Figure `.Text` instances
# ================ ============================================================
# .. _axes-container:
# Axes container
# --------------
# The :class:`matplotlib.axes.Axes` is the center of the Matplotlib
# universe -- it contains the vast majority of all the ``Artists`` used
# in a figure with many helper methods to create and add these
# ``Artists`` to itself, as well as helper methods to access and
# customize the ``Artists`` it contains.  Like the
# :class:`~matplotlib.figure.Figure`, it contains a
# :class:`~matplotlib.patches.Patch`
# :attr:`~matplotlib.axes.Axes.patch` which is a
# :class:`~matplotlib.patches.Rectangle` for Cartesian coordinates and a
# :class:`~matplotlib.patches.Circle` for polar coordinates; this patch
# determines the shape, background and border of the plotting region::
#     ax = fig.add_subplot()
#     rect = ax.patch  # a Rectangle instance
#     rect.set_facecolor('green')
# When you call a plotting method, e.g., the canonical
# `~matplotlib.axes.Axes.plot` and pass in arrays or lists of values, the
# method will create a `matplotlib.lines.Line2D` instance, update the line with
# all the ``Line2D`` properties passed as keyword arguments, add the line to
# the ``Axes``, and return it to you:
# .. sourcecode:: ipython
#     In [213]: x, y = np.random.rand(2, 100)
#     In [214]: line, = ax.plot(x, y, '-', color='blue', linewidth=2)
# ``plot`` returns a list of lines because you can pass in multiple x, y
# pairs to plot, and we are unpacking the first element of the length
# one list into the line variable.  The line has been added to the
# ``Axes.lines`` list:
# .. sourcecode:: ipython
#     In [229]: print(ax.lines)
#     [<matplotlib.lines.Line2D at 0xd378b0c>]
# Similarly, methods that create patches, like
# :meth:`~matplotlib.axes.Axes.bar` creates a list of rectangles, will
# add the patches to the :attr:`Axes.patches
# <matplotlib.axes.Axes.patches>` list:
# .. sourcecode:: ipython
#     In [233]: n, bins, rectangles = ax.hist(np.random.randn(1000), 50)
#     In [234]: rectangles
#     Out[234]: <BarContainer object of 50 artists>
#     In [235]: print(len(ax.patches))
#     Out[235]: 50
# You should not add objects directly to the ``Axes.lines`` or ``Axes.patches``
# lists, because the ``Axes`` needs to do a few things when it creates and adds
# an object:
# - It sets the ``figure`` and ``axes`` property of the ``Artist``;
# - It sets the default ``Axes`` transformation (unless one is already set);
# - It inspects the data contained in the ``Artist`` to update the data
#   structures controlling auto-scaling, so that the view limits can be
#   adjusted to contain the plotted data.
# You can, nonetheless, create objects yourself and add them directly to the
# ``Axes`` using helper methods like `~matplotlib.axes.Axes.add_line` and
# `~matplotlib.axes.Axes.add_patch`.  Here is an annotated interactive session
# illustrating what is going on:
# .. sourcecode:: ipython
#     In [262]: fig, ax = plt.subplots()
#     # create a rectangle instance
#     In [263]: rect = matplotlib.patches.Rectangle((1, 1), width=5, height=12)
#     # by default the axes instance is None
#     In [264]: print(rect.axes)
#     # and the transformation instance is set to the "identity transform"
#     In [265]: print(rect.get_data_transform())
#     IdentityTransform()
#     # now we add the Rectangle to the Axes
#     In [266]: ax.add_patch(rect)
#     # and notice that the ax.add_patch method has set the axes
#     # instance
#     In [267]: print(rect.axes)
#     Axes(0.125,0.1;0.775x0.8)
#     # and the transformation has been set too
#     In [268]: print(rect.get_data_transform())
#     CompositeGenericTransform(
#         TransformWrapper(
#             BlendedAffine2D(
#                 IdentityTransform(),
#                 IdentityTransform())),
#         CompositeGenericTransform(
#             BboxTransformFrom(
#                 TransformedBbox(
#                     Bbox(x0=0.0, y0=0.0, x1=1.0, y1=1.0),
#                     TransformWrapper(
#                         BlendedAffine2D(
#                             IdentityTransform(),
#                             IdentityTransform())))),
#             BboxTransformTo(
#                 TransformedBbox(
#                     Bbox(x0=0.125, y0=0.10999999999999999, x1=0.9, y1=0.88),
#                     BboxTransformTo(
#                         TransformedBbox(
#                             Bbox(x0=0.0, y0=0.0, x1=6.4, y1=4.8),
#                             Affine2D(
#                                  [  0.   0.   1.]])))))))
#     # the default axes transformation is ax.transData
#     In [269]: print(ax.transData)
#     CompositeGenericTransform(
#         TransformWrapper(
#             BlendedAffine2D(
#                 IdentityTransform(),
#                 IdentityTransform())),
#         CompositeGenericTransform(
#             BboxTransformFrom(
#                 TransformedBbox(
#                     Bbox(x0=0.0, y0=0.0, x1=1.0, y1=1.0),
#                     TransformWrapper(
#                         BlendedAffine2D(
#                             IdentityTransform(),
#                             IdentityTransform())))),
#             BboxTransformTo(
#                 TransformedBbox(
#                     Bbox(x0=0.125, y0=0.10999999999999999, x1=0.9, y1=0.88),
#                     BboxTransformTo(
#                         TransformedBbox(
#                             Bbox(x0=0.0, y0=0.0, x1=6.4, y1=4.8),
#                             Affine2D(
#                                  [  0.   0.   1.]])))))))
#     # notice that the xlimits of the Axes have not been changed
#     In [270]: print(ax.get_xlim())
#     (0.0, 1.0)
#     # but the data limits have been updated to encompass the rectangle
#     In [271]: print(ax.dataLim.bounds)
#     (1.0, 1.0, 5.0, 12.0)
#     # we can manually invoke the auto-scaling machinery
#     In [272]: ax.autoscale_view()
#     # and now the xlim are updated to encompass the rectangle, plus margins
#     In [273]: print(ax.get_xlim())
#     (0.75, 6.25)
#     # we have to manually force a figure draw
#     In [274]: fig.canvas.draw()
# There are many, many ``Axes`` helper methods for creating primitive
# ``Artists`` and adding them to their respective containers.  The table
# below summarizes a small sampling of them, the kinds of ``Artist`` they
# create, and where they store them
# =========================================  =================  ===============
# Axes helper method                         Artist             Container
# =========================================  =================  ===============
# `~.axes.Axes.annotate` - text annotations  `.Annotation`      ax.texts
# `~.axes.Axes.bar` - bar charts             `.Rectangle`       ax.patches
# `~.axes.Axes.errorbar` - error bar plots   `.Line2D` and      ax.lines and
#                                            `.Rectangle`       ax.patches
# `~.axes.Axes.fill` - shared area           `.Polygon`         ax.patches
# `~.axes.Axes.hist` - histograms            `.Rectangle`       ax.patches
# `~.axes.Axes.imshow` - image data          `.AxesImage`       ax.images
# `~.axes.Axes.legend` - Axes legend         `.Legend`          ax.get_legend()
# `~.axes.Axes.plot` - xy plots              `.Line2D`          ax.lines
# `~.axes.Axes.scatter` - scatter charts     `.PolyCollection`  ax.collections
# `~.axes.Axes.text` - text                  `.Text`            ax.texts
# =========================================  =================  ===============
# In addition to all of these ``Artists``, the ``Axes`` contains two
# important ``Artist`` containers: the :class:`~matplotlib.axis.XAxis`
# and :class:`~matplotlib.axis.YAxis`, which handle the drawing of the
# ticks and labels.  These are stored as instance variables
# :attr:`~matplotlib.axes.Axes.xaxis` and
# :attr:`~matplotlib.axes.Axes.yaxis`.  The ``XAxis`` and ``YAxis``
# containers will be detailed below, but note that the ``Axes`` contains
# many helper methods which forward calls on to the
# :class:`~matplotlib.axis.Axis` instances so you often do not need to
# work with them directly unless you want to.  For example, you can set
# the font color of the ``XAxis`` ticklabels using the ``Axes`` helper
#     ax.tick_params(axis='x', labelcolor='orange')
# Below is a summary of the Artists that the `~.axes.Axes` contains
# ==============    =========================================
# Axes attribute    Description
# ==============    =========================================
# artists           An `.ArtistList` of `.Artist` instances
# patch             `.Rectangle` instance for Axes background
# collections       An `.ArtistList` of `.Collection` instances
# images            An `.ArtistList` of `.AxesImage`
# lines             An `.ArtistList` of `.Line2D` instances
# patches           An `.ArtistList` of `.Patch` instances
# texts             An `.ArtistList` of `.Text` instances
# xaxis             A `matplotlib.axis.XAxis` instance
# yaxis             A `matplotlib.axis.YAxis` instance
# ==============    =========================================
# The legend can be accessed by `~.axes.Axes.get_legend`,
# .. _axis-container:
# Axis containers
# ---------------
# The :class:`matplotlib.axis.Axis` instances handle the drawing of the
# tick lines, the grid lines, the tick labels and the axis label.  You
# can configure the left and right ticks separately for the y-axis, and
# the upper and lower ticks separately for the x-axis.  The ``Axis``
# also stores the data and view intervals used in auto-scaling, panning
# 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:`.pyplot` modifies in calls to `~.pyplot.xlabel` and
# `~.pyplot.ylabel`) as well as a list of major and minor ticks.  The ticks are
# `.axis.XTick` and `.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
# `.axis.Axis.get_major_ticks` and `.axis.Axis.get_minor_ticks`.  Although
# the ticks contain all the primitives and will be covered below, ``Axis``
# instances have accessor methods that return the tick lines, tick labels, tick
# locations etc.:
fig, ax = plt.subplots()
axis = ax.xaxis
axis.get_ticklocs()
###############################################################################
axis.get_ticklabels()
###############################################################################
# note there are twice as many ticklines as labels because by default there are
# tick lines at the top and bottom but only tick labels below the xaxis;
# however, this can be customized.
axis.get_ticklines()
###############################################################################
# And with the above methods, you only get lists of major ticks back by
# default, but you can also ask for the minor ticks:
axis.get_ticklabels(minor=True)
axis.get_ticklines(minor=True)
###############################################################################
# Here is a summary of some of the useful accessor methods of the ``Axis``
# (these have corresponding setters where useful, such as
# :meth:`~matplotlib.axis.Axis.set_major_formatter`.)
# =============================  ==============================================
# Axis accessor method           Description
# =============================  ==============================================
# `~.Axis.get_scale`             The scale of the Axis, e.g., 'log' or 'linear'
# `~.Axis.get_view_interval`     The interval instance of the Axis view limits
# `~.Axis.get_data_interval`     The interval instance of the Axis data limits
# `~.Axis.get_gridlines`         A list of grid lines for the Axis
# `~.Axis.get_label`             The Axis label - a `.Text` instance
# `~.Axis.get_offset_text`       The Axis offset text - a `.Text` instance
# `~.Axis.get_ticklabels`        A list of `.Text` instances -
#                                keyword minor=True|False
# `~.Axis.get_ticklines`         A list of `.Line2D` instances -
#                                keyword minor=True|False
# `~.Axis.get_ticklocs`          A list of Tick locations -
#                                keyword minor=True|False
# `~.Axis.get_major_locator`     The `.ticker.Locator` instance for major ticks
# `~.Axis.get_major_formatter`   The `.ticker.Formatter` instance for major
# `~.Axis.get_minor_locator`     The `.ticker.Locator` instance for minor ticks
# `~.Axis.get_minor_formatter`   The `.ticker.Formatter` instance for minor
# `~.axis.Axis.get_major_ticks`  A list of `.Tick` instances for major ticks
# `~.axis.Axis.get_minor_ticks`  A list of `.Tick` instances for minor ticks
# `~.Axis.grid`                  Turn the grid on or off for the major or minor
# =============================  ==============================================
# Here is an example, not recommended for its beauty, which customizes
# the Axes and Tick properties.
# plt.figure creates a matplotlib.figure.Figure instance
fig = plt.figure()
rect = fig.patch  # a rectangle instance
rect.set_facecolor('lightgoldenrodyellow')
ax1 = fig.add_axes([0.1, 0.3, 0.4, 0.4])
rect = ax1.patch
rect.set_facecolor('lightslategray')
for label in ax1.xaxis.get_ticklabels():
    # label is a Text instance
    label.set_color('red')
    label.set_rotation(45)
    label.set_fontsize(16)
for line in ax1.yaxis.get_ticklines():
    # line is a Line2D instance
    line.set_color('green')
    line.set_markersize(25)
    line.set_markeredgewidth(3)
###############################################################################
# .. _tick-container:
# Tick containers
# ---------------
# The :class:`matplotlib.axis.Tick` is the final container object in our
# descent from the :class:`~matplotlib.figure.Figure` to the
# :class:`~matplotlib.axes.Axes` to the :class:`~matplotlib.axis.Axis`
# to the :class:`~matplotlib.axis.Tick`.  The ``Tick`` contains the tick
# and grid line instances, as well as the label instances for the upper
# and lower ticks.  Each of these is accessible directly as an attribute
# of the ``Tick``.
# ==============  ==========================================================
# Tick attribute  Description
# ==============  ==========================================================
# tick1line       A `.Line2D` instance
# tick2line       A `.Line2D` instance
# gridline        A `.Line2D` instance
# label1          A `.Text` instance
# label2          A `.Text` instance
# ==============  ==========================================================
# Here is an example which sets the formatter for the right side ticks with
# dollar signs and colors them green on the right side of the yaxis.
# .. include:: ../../gallery/ticks/dollar_ticks.rst
#    :start-after: y axis labels.
#    :end-before: .. admonition:: References
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