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# Artist tutorial

Using Artist objects to render on the canvas.

There are three layers to the matplotlib API.

- the `matplotlib.backend_bases.FigureCanvas` is the area onto which the figure is drawn
- the `matplotlib.backend_bases.Renderer` is the object which knows how to draw on the `FigureCanvas`
- and the `matplotlib.artist.Artist` is the object that knows how to use a renderer to paint onto the canvas.

The `FigureCanvas` and `Renderer` handle all the details of talking to user interface toolkits like wxPython 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: `Line2D`, `Rectangle`, `Text`, `AxesImage`, etc., and the containers are places to put them (`Axis`, `Axes` and `Figure`). The standard use is to create a `Figure` instance, use the `Figure` to create one or more `Axes` or `Subplot` instances, and use the `Axes` instance helper methods to create the primitives. In the example below, we create a `Figure` instance using `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 `pyplot` handle some of those details for us:

    import matplotlib.pyplot as plt
    fig = plt.figure()
    ax = fig.add_subplot(2,1,1) # two rows, one column, first plot

The `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 (`plot()`, `text()`, `hist()`, `imshow()`) to create the most common graphics primitives (`Line2D`, `Text`, `Rectangle`, `Image`, 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 `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 `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.lines` list. In the interactive ipython 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:

    In [101]: ax.lines[0]
    Out[101]: <matplotlib.lines.Line2D instance at 0x19a95710>

    In [102]: line
    Out[102]: <matplotlib.lines.Line2D instance 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 lines later simply by calling the list methods; either of these will work:

    del ax.lines[0]
    ax.lines.remove(line)  # one or the other, not both!

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 `ax.set_xlabel`, it passes the information on the `Text` instance of the `XAxis`. Each `Axes` instance contains an `XAxis` and a `YAxis` instance, which handle the layout and drawing of the ticks, tick labels and axis labels.

Try creating the figure below.

    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('volts')
    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)')

    plt.show()

## Customizing your objects

Every element in the figure is represented by a matplotlib `Artist`, and each has an extensive list of properties to configure its appearance. The figure itself contains a `Rectangle` exactly the size of the figure, which you can use to set the background color and transparency of the figures. Likewise, each `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 `Figure.patch` and `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 rastergraphics: (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 `matplotlib.artist.getp()` function (simply `getp()` in pylab), 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:

    In [149]: matplotlib.artist.getp(fig.patch)
        alpha = 1.0
        animated = False
        antialiased or aa = True
        axes = None
        clip_box = None
        clip_on = False
        clip_path = None
        contains = None
        edgecolor or ec = w
        facecolor or fc = 0.75
        figure = Figure(8.125x6.125)
        fill = 1
        hatch = None
        height = 1
        label =
        linewidth or lw = 1.0
        picker = None
        transform = <Affine object at 0x134cca84>
        verts = ((0, 0), (0, 1), (1, 1), (1, 0))
        visible = True
        width = 1
        window_extent = <Bbox object at 0x134acbcc>
        x = 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 artist Module for a listing of properties for a given object.

## 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 `Text` instance, the width of a `Line2D`) although the containers also have some properties as well – for example the `Axes` `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

The top level container `Artist` is the `matplotlib.figure.Figure`, and it contains everything in the figure. The background of the figure is a `Rectangle` which is stored in `Figure.patch`. As you add subplots (`add_subplot()`) and axes (`add_axes()`) to the figure these will be appended to the `Figure.axes`. These are also returned by the methods that create them:

    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]: <matplotlib.axes.Subplot instance at 0xd54b26c>

    In [160]: print fig.axes
    [<matplotlib.axes.Subplot instance at 0xd54b26c>, <matplotlib.axes.Axes instance at 0xd3f0b2c>]

Because the figure maintains the concept of the “current axes” (see `Figure.gca` and `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 `add_subplot()` and `add_axes()` methods to insert, and the `delaxes()` 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 text, lines, patches and images, which you can use to add primitives directly. The default coordinate system for the `Figure` will simply be in pixels (which is not usually what you want) but you can control this by setting the transform property of the `Artist` you are adding to the figure.

More useful is “figure coordinates” where (0, 0) is the bottom-left of the figure and (1, 1) is the top-right of the figure which you can obtain by setting the `Artist` transform to `fig.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])

    plt.show()

Here is a summary of the Artists the figure contains

Figure attribute | Description
--- | ---
axes | A list of Axes instances (includes Subplot)
patch | The Rectangle background
images | A list of FigureImages patches - useful for raw pixel display
legends | A list of Figure Legend instances (different from Axes.legends)
lines | A list of Figure Line2D instances (rarely used, see Axes.lines)
patches | A list of Figure patches (rarely used, see Axes.patches)
texts | A list Figure Text instances

## Axes container

The `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 `Figure`, it contains a `Patch` `patch` which is a `Rectangle` for Cartesian coordinates and a `Circle` for polar coordinates; this patch determines the shape, background and border of the plotting region:

    ax = fig.add_subplot(111)
    rect = ax.patch  # a Rectangle instance
    rect.set_facecolor('green')

When you call a plotting method, e.g., the canonical `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.lines` container, and returns it to you:

    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:

    In [229]: print ax.lines
    [<matplotlib.lines.Line2D instance at 0xd378b0c>]

Similarly, methods that create patches, like `bar()` creates a list of rectangles, will add the patches to the `Axes.patches` list:

    In [233]: n, bins, rectangles = ax.hist(np.random.randn(1000), 50, facecolor='yellow')

    In [234]: rectangles
    Out[234]: <a list of 50 Patch objects>

    In [235]: print len(ax.patches)

You should not add objects directly to the `Axes.lines` or `Axes.patches` lists unless you know exactly what you are doing, 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`, as well as the default `Axes` transformation (unless a transformation is set). It also 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 `add_line()` and `add_patch()`. Here is an annotated interactive session illustrating what is going on:

    In [261]: fig = plt.figure()

    In [262]: ax = fig.add_subplot(111)

    # 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.get_axes()
    None

    # and the transformation instance is set to the "identity transform"
    In [265]: print rect.get_transform()
    <Affine object at 0x13695544>

    # 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.get_axes()
    Axes(0.125,0.1;0.775x0.8)

    # and the transformation has been set too
    In [268]: print rect.get_transform()
    <Affine object at 0x15009ca4>

    # the default axes transformation is ax.transData
    In [269]: print ax.transData
    <Affine object at 0x15009ca4>

    # 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
    In [273]: print ax.get_xlim()
    (1.0, 6.0)

    # we have to manually force a figure draw
    In [274]: ax.figure.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

Helper method | Artist | Container
--- | --- | ---
ax.annotate - text annotations | Annotate | ax.texts
ax.bar - bar charts | Rectangle | ax.patches
ax.errorbar - error bar plots | Line2D and Rectangle | ax.lines and ax.patches
ax.fill - shared area | Polygon | ax.patches
ax.hist - histograms | Rectangle | ax.patches
ax.imshow - image data | AxesImage | ax.images
ax.legend - axes legends | Legend | ax.legends
ax.plot - xy plots | Line2D | ax.lines
ax.scatter - scatter charts | PolygonCollection | ax.collections
ax.text - text | Text | ax.texts

In addition to all of these `Artists`, the `Axes` contains two important `Artist` containers: the `XAxis` and `YAxis`, which handle the drawing of the ticks and labels. These are stored as instance variables `xaxis` and `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 `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 method:

    for label in ax.get_xticklabels():
        label.set_color('orange')

Below is a summary of the Artists that the Axes contains

Axes attribute | Description
--- | ---
artists | A list of Artist instances
patch | Rectangle instance for Axes background
collections | A list of Collection instances
images | A list of AxesImage
legends | A list of Legend instances
lines | A list of Line2D instances
patches | A list of Patch instances
texts | A list of Text instances
xaxis | matplotlib.axis.XAxis instance
yaxis | matplotlib.axis.YAxis instance

## Axis containers

The `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 `Locator` and `Formatter` instances which control where the ticks are placed and how they are represented as strings.

Each `Axis` object contains a `label` attribute (this is what `pylab` modifies in calls to `xlabel()` and `ylabel()`) as well as a list of major and minor ticks. The ticks are `XTick` and `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 `get_major_ticks()` and `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; this can be customized

    axis.get_ticklines()

by default you get the major ticks back

    axis.get_ticklines()

but you can also ask for the minor ticks

    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
    # set_major_formatter)
    #
    # ======================  =========================================================
    # Accessor method         Description
    # ======================  =========================================================
    # get_scale               The scale of the axis, e.g., 'log' or 'linear'
    # get_view_interval       The interval instance of the axis view limits
    # get_data_interval       The interval instance of the axis data limits
    # get_gridlines           A list of grid lines for the Axis
    # get_label               The axis label - a Text instance
    # get_ticklabels          A list of Text instances - keyword minor=True|False
    # get_ticklines           A list of Line2D instances - keyword minor=True|False
    # get_ticklocs            A list of Tick locations - keyword minor=True|False
    # get_major_locator       The matplotlib.ticker.Locator instance for major ticks
    # get_major_formatter     The matplotlib.ticker.Formatter instance for major ticks
    # get_minor_locator       The matplotlib.ticker.Locator instance for minor ticks
    # get_minor_formatter     The matplotlib.ticker.Formatter instance for minor ticks
    # get_major_ticks         A list of Tick instances for major ticks
    # get_minor_ticks         A list of Tick instances for minor ticks
    # grid                    Turn the grid on or off for the major or minor ticks
    # ======================  =========================================================
    #
    # 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)

    plt.show()

## Tick containers

The `matplotlib.axis.Tick` is the final container object in our descent from the `Figure` to the `Axes` to the `Axis` to the `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`. In addition, there are boolean variables that determine whether the upper labels and ticks are on for the x-axis and whether the right labels and ticks are on for the y-axis.

Tick attribute | Description
--- | ---
tick1line | Line2D instance
tick2line | Line2D instance
gridline | Line2D instance
label1 | Text instance
label2 | Text instance
gridOn | boolean which determines whether to draw the gridline
tick1On | boolean which determines whether to draw the 1st tickline
tick2On | boolean which determines whether to draw the 2nd tickline
label1On | boolean which determines whether to draw the 1st tick label
label2On | boolean which determines whether to draw the 2nd tick label

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

    import matplotlib.ticker as ticker

    # Fixing random state for reproducibility
    np.random.seed(19680801)

    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.plot(100*np.random.rand(20))

    formatter = ticker.FormatStrFormatter('$%1.2f')
    ax.yaxis.set_major_formatter(formatter)

    for tick in ax.yaxis.get_major_ticks():
        tick.label1On = False
        tick.label2On = True
        tick.label2.set_color('green')

    plt.show()

Total running time of the script: ( 0 minutes 0.154 seconds)

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