Annotations

Annotating text with Matplotlib.

Table of Contents

• Annotations
  • Basic annotation
  • Advanced Annotations
    • Annotating with Text with Box
    • Annotating with Arrow
    • Placing Artist at the anchored location of the Axes
    • Using Complex Coordinates with Annotations
    • Using ConnectionPatch
  • Advanced Topics
    • Zoom effect between Axes
    • Define Custom BoxStyle

Basic annotation

The uses of the basic text() will place text at an arbitrary position on the Axes. A common use case of text is to annotate some feature of the plot, and the annotate() method provides helper functionality to make annotations easy. In an annotation, there are two points to consider: the location being annotated represented by the argument xy and the location of the text xytext. Both of these arguments are (x, y) tuples.

Annotation Basic

In this example, both the xy (arrow tip) and xytext locations (text location) are in data coordinates. There are a variety of other coordinate systems one can choose -- you can specify the coordinate system of xy and xytext with one of the following strings for xycoords and textcoords (default is 'data')

argument	coordinate system
'figure points'	points from the lower left corner of the figure
'figure pixels'	pixels from the lower left corner of the figure
'figure fraction'	(0, 0) is lower left of figure and (1, 1) is upper right
'axes points'	points from lower left corner of axes
'axes pixels'	pixels from lower left corner of axes
'axes fraction'	(0, 0) is lower left of axes and (1, 1) is upper right
'data'	use the axes data coordinate system

For example to place the text coordinates in fractional axes coordinates, one could do:

ax.annotate('local max', xy=(3, 1),  xycoords='data',
            xytext=(0.8, 0.95), textcoords='axes fraction',
            arrowprops=dict(facecolor='black', shrink=0.05),
            horizontalalignment='right', verticalalignment='top',
            )

For physical coordinate systems (points or pixels) the origin is the bottom-left of the figure or axes.

Optionally, you can enable drawing of an arrow from the text to the annotated point by giving a dictionary of arrow properties in the optional keyword argument arrowprops.

arrowprops key	description
width	the width of the arrow in points
frac	the fraction of the arrow length occupied by the head
headwidth	the width of the base of the arrow head in points
shrink	move the tip and base some percent away from the annotated point and text
**kwargs	any key for matplotlib.patches.Polygon, e.g., facecolor

In the example below, the xy point is in native coordinates (xycoords defaults to 'data'). For a polar axes, this is in (theta, radius) space. The text in this example is placed in the fractional figure coordinate system. matplotlib.text.Text keyword arguments like horizontalalignment, verticalalignment and fontsize are passed from annotate to the Text instance.

Annotation Polar

For more on all the wild and wonderful things you can do with annotations, including fancy arrows, see Advanced Annotations and Annotating Plots.

Do not proceed unless you have already read Basic annotation, text() and annotate()!

Advanced Annotations

Annotating with Text with Box

Let's start with a simple example.

Annotate Text Arrow

text takes a bbox keyword argument, which draws a box around the text:

t = ax.text(
    0, 0, "Direction", ha="center", va="center", rotation=45, size=15,
    bbox=dict(boxstyle="rarrow,pad=0.3", fc="cyan", ec="b", lw=2))

The patch object associated with the text can be accessed by:

bb = t.get_bbox_patch()

The return value is a FancyBboxPatch; patch properties (facecolor, edgewidth, etc.) can be accessed and modified as usual. FancyBboxPatch.set_boxstyle sets the box shape:

bb.set_boxstyle("rarrow", pad=0.6)

The arguments are the name of the box style with its attributes as keyword arguments. Currently, following box styles are implemented.

Class	Name	Attrs
Circle	circle	pad=0.3
DArrow	darrow	pad=0.3
LArrow	larrow	pad=0.3
RArrow	rarrow	pad=0.3
Round	round	pad=0.3,rounding_size=None
Round4	round4	pad=0.3,rounding_size=None
Roundtooth	roundtooth	pad=0.3,tooth_size=None
Sawtooth	sawtooth	pad=0.3,tooth_size=None
Square	square	pad=0.3

Fancybox Demo

Note that the attribute arguments can be specified within the style name with separating comma (this form can be used as "boxstyle" value of bbox argument when initializing the text instance)

bb.set_boxstyle("rarrow,pad=0.6")

Annotating with Arrow

annotate draws an arrow connecting two points in an axes:

ax.annotate("Annotation",
            xy=(x1, y1), xycoords='data',
            xytext=(x2, y2), textcoords='offset points',
            )

This annotates a point at xy in the given coordinate (xycoords) with the text at xytext given in textcoords. Often, the annotated point is specified in the data coordinate and the annotating text in offset points. See annotate for available coordinate systems.

An arrow connecting xy to xytext can be optionally drawn by specifying the arrowprops argument. To draw only an arrow, use empty string as the first argument.

ax.annotate("",
            xy=(0.2, 0.2), xycoords='data',
            xytext=(0.8, 0.8), textcoords='data',
            arrowprops=dict(arrowstyle="->",
                            connectionstyle="arc3"),
            )

Annotate Simple01

The arrow is drawn as follows:

1. A path connecting the two points is created, as specified by the connectionstyle parameter.
2. The path is clipped to avoid patches patchA and patchB, if these are set.
3. The path is further shrunk by shrinkA and shrinkB (in pixels).
4. The path is transmuted to an arrow patch, as specified by the arrowstyle parameter.

Annotate Explain

The creation of the connecting path between two points is controlled by connectionstyle key and the following styles are available.

Name	Attrs
angle	angleA=90,angleB=0,rad=0.0
angle3	angleA=90,angleB=0
arc	angleA=0,angleB=0,armA=None,armB=None,rad=0.0
arc3	rad=0.0
bar	armA=0.0,armB=0.0,fraction=0.3,angle=None

Note that "3" in angle3 and arc3 is meant to indicate that the resulting path is a quadratic spline segment (three control points). As will be discussed below, some arrow style options can only be used when the connecting path is a quadratic spline.

The behavior of each connection style is (limitedly) demonstrated in the example below. (Warning: The behavior of the bar style is currently not well defined, it may be changed in the future).

Connectionstyle Demo

The connecting path (after clipping and shrinking) is then mutated to an arrow patch, according to the given arrowstyle.

Name	Attrs
-	None
->	head_length=0.4,head_width=0.2
-[	widthB=1.0,lengthB=0.2,angleB=None
|-|	widthA=1.0,widthB=1.0
-|>	head_length=0.4,head_width=0.2
<-	head_length=0.4,head_width=0.2
<->	head_length=0.4,head_width=0.2
<|-	head_length=0.4,head_width=0.2
<|-|>	head_length=0.4,head_width=0.2
fancy	head_length=0.4,head_width=0.4,tail_width=0.4
simple	head_length=0.5,head_width=0.5,tail_width=0.2
wedge	tail_width=0.3,shrink_factor=0.5

Fancyarrow Demo

Some arrowstyles only work with connection styles that generate a quadratic-spline segment. They are fancy, simple, and wedge. For these arrow styles, you must use the "angle3" or "arc3" connection style.

If the annotation string is given, the patchA is set to the bbox patch of the text by default.

Annotate Simple02

As with text, a box around the text can be drawn using the bbox argument.

Annotate Simple03

By default, the starting point is set to the center of the text extent. This can be adjusted with relpos key value. The values are normalized to the extent of the text. For example, (0, 0) means lower-left corner and (1, 1) means top-right.

Annotate Simple04

Placing Artist at the anchored location of the Axes

There are classes of artists that can be placed at an anchored location in the Axes. A common example is the legend. This type of artist can be created by using the OffsetBox class. A few predefined classes are available in matplotlib.offsetbox and in mpl_toolkits.axes_grid1.anchored_artists.

from matplotlib.offsetbox import AnchoredText
at = AnchoredText("Figure 1a",
                  prop=dict(size=15), frameon=True,
                  loc='upper left',
                  )
at.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
ax.add_artist(at)

Anchored Box01

The loc keyword has same meaning as in the legend command.

A simple application is when the size of the artist (or collection of artists) is known in pixel size during the time of creation. For example, If you want to draw a circle with fixed size of 20 pixel x 20 pixel (radius = 10 pixel), you can utilize AnchoredDrawingArea. The instance is created with a size of the drawing area (in pixels), and arbitrary artists can added to the drawing area. Note that the extents of the artists that are added to the drawing area are not related to the placement of the drawing area itself. Only the initial size matters.

from mpl_toolkits.axes_grid1.anchored_artists import AnchoredDrawingArea

ada = AnchoredDrawingArea(20, 20, 0, 0,
                          loc='upper right', pad=0., frameon=False)
p1 = Circle((10, 10), 10)
ada.drawing_area.add_artist(p1)
p2 = Circle((30, 10), 5, fc="r")
ada.drawing_area.add_artist(p2)

The artists that are added to the drawing area should not have a transform set (it will be overridden) and the dimensions of those artists are interpreted as a pixel coordinate, i.e., the radius of the circles in above example are 10 pixels and 5 pixels, respectively.

Anchored Box02

Sometimes, you want your artists to scale with the data coordinate (or coordinates other than canvas pixels). You can use AnchoredAuxTransformBox class. This is similar to AnchoredDrawingArea except that the extent of the artist is determined during the drawing time respecting the specified transform.

from mpl_toolkits.axes_grid1.anchored_artists import AnchoredAuxTransformBox

box = AnchoredAuxTransformBox(ax.transData, loc='upper left')
el = Ellipse((0, 0), width=0.1, height=0.4, angle=30)  # in data coordinates!
box.drawing_area.add_artist(el)

The ellipse in the above example will have width and height corresponding to 0.1 and 0.4 in data coordinates and will be automatically scaled when the view limits of the axes change.

Anchored Box03

As in the legend, the bbox_to_anchor argument can be set. Using the HPacker and VPacker, you can have an arrangement(?) of artist as in the legend (as a matter of fact, this is how the legend is created).

Anchored Box04

Note that unlike the legend, the bbox_transform is set to IdentityTransform by default.

Using Complex Coordinates with Annotations

The Annotation in matplotlib supports several types of coordinates as described in Basic annotation. For an advanced user who wants more control, it supports a few other options.

1. A Transform instance. For example,

   ax.annotate("Test", xy=(0.5, 0.5), xycoords=ax.transAxes)
   

   is identical to

   ax.annotate("Test", xy=(0.5, 0.5), xycoords="axes fraction")
   

   This allows annotating a point in another axes:

   ax1, ax2 = subplot(121), subplot(122)
   ax2.annotate("Test", xy=(0.5, 0.5), xycoords=ax1.transData,
                xytext=(0.5, 0.5), textcoords=ax2.transData,
                arrowprops=dict(arrowstyle="->"))
   

2. An Artist instance. The xy value (or xytext) is interpreted as a fractional coordinate of the bbox (return value of get_window_extent) of the artist:

   an1 = ax.annotate("Test 1", xy=(0.5, 0.5), xycoords="data",
                     va="center", ha="center",
                     bbox=dict(boxstyle="round", fc="w"))
   an2 = ax.annotate("Test 2", xy=(1, 0.5), xycoords=an1,  # (1, 0.5) of the an1's bbox
                     xytext=(30, 0), textcoords="offset points",
                     va="center", ha="left",
                     bbox=dict(boxstyle="round", fc="w"),
                     arrowprops=dict(arrowstyle="->"))
   

   

   Annotation with Simple Coordinates

   Note that you must ensure that the extent of the coordinate artist (an1 in above example) is determined before an2 gets drawn. Usually, this means that an2 needs to be drawn after an1.

3. A callable object that takes the renderer instance as single argument, and returns either a Transform or a BboxBase. The return value is then handled as in (1), for transforms, or in (2), for bboxes. For example,

   an2 = ax.annotate("Test 2", xy=(1, 0.5), xycoords=an1,
                     xytext=(30, 0), textcoords="offset points")
   

   is identical to:

   an2 = ax.annotate("Test 2", xy=(1, 0.5), xycoords=an1.get_window_extent,
                     xytext=(30, 0), textcoords="offset points")
   

4. A pair of coordinate specifications -- the first for the x-coordinate, and the second is for the y-coordinate; e.g.

   annotate("Test", xy=(0.5, 1), xycoords=("data", "axes fraction"))
   

   Here, 0.5 is in data coordinates, and 1 is in normalized axes coordinates. Each of the coordinate specifications can also be an artist or a transform. For example,

   

   Annotation with Simple Coordinates 2

5. Sometimes, you want your annotation with some "offset points", not from the annotated point but from some other point. text.OffsetFrom is a helper for such cases.

   

   Annotation with Simple Coordinates 3

   You may take a look at this example Annotating Plots.

Using ConnectionPatch

ConnectionPatch is like an annotation without text. While annotate is sufficient in most situations, ConnectionPatch is useful when you want to connect points in different axes.

from matplotlib.patches import ConnectionPatch
xy = (0.2, 0.2)
con = ConnectionPatch(xyA=xy, coordsA=ax1.transData,
                      xyB=xy, coordsB=ax2.transData)
fig.add_artist(con)

The above code connects point xy in the data coordinates of ax1 to point xy in the data coordinates of ax2. Here is a simple example.

Connect Simple01

Here, we added the ConnectionPatch to the figure (with add_artist) rather than to either axes: this ensures that it is drawn on top of both axes, and is also necessary if using constrained_layout for positioning the axes.

Advanced Topics

Zoom effect between Axes

mpl_toolkits.axes_grid1.inset_locator defines some patch classes useful for interconnecting two axes. Understanding the code requires some knowledge of Matplotlib's transform system.

Axes Zoom Effect

Define Custom BoxStyle

You can use a custom box style. The value for the boxstyle can be a callable object in the following forms.:

def __call__(self, x0, y0, width, height, mutation_size,
             aspect_ratio=1.):
    '''
    Given the location and size of the box, return the path of
    the box around it.

      - *x0*, *y0*, *width*, *height* : location and size of the box
      - *mutation_size* : a reference scale for the mutation.
      - *aspect_ratio* : aspect-ratio for the mutation.
    '''
    path = ...
    return path

Here is a complete example.

Custom Boxstyle01

Similarly, you can define a custom ConnectionStyle and a custom ArrowStyle. See the source code of lib/matplotlib/patches.py and check how each style class is defined.