Annotating text with Matplotlib.
Table of Contents
- Basic annotation
- Advanced Annotation 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
- 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
- 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
In this example, both the
xy (arrow tip) and
(text location) are in data coordinates. There are a variety of other
coordinate systems one can choose -- you can specify the coordinate
xytext with one of the following strings for
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
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.
keyword args like
fontsize are passed from
annotate to the
# Advanced Annotation
# Annotating with Text with Box
Let's start with a simple example.
Annotate Text Arrow
text() function in the pyplot module (or
text method of the Axes class) takes bbox keyword argument, and when
given, a box around the text is drawn.
bbox_props = dict(boxstyle="rarrow,pad=0.3", fc="cyan", ec="b", lw=2) t = ax.text(0, 0, "Direction", ha="center", va="center", rotation=45, size=15, bbox=bbox_props)
The patch object associated with the text can be accessed by:
bb = t.get_bbox_patch()
The return value is an instance of FancyBboxPatch and the patch properties like facecolor, edgewidth, etc. can be accessed and modified as usual. To change the shape of the box, use the set_boxstyle method.
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
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)
# Annotating with Arrow
annotate() function in the pyplot module
(or annotate method of the Axes class) is used to draw an arrow
connecting two points on the plot.
ax.annotate("Annotation", xy=(x1, y1), xycoords='data', xytext=(x2, y2), textcoords='offset points', )
This annotates a point at
xy in the given coordinate (
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.
annotate() for available coordinate systems.
An arrow connecting two points (xy & xytext) can be optionally drawn by
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"), )
The arrow drawing takes a few steps.
- a connecting path between two points are created. This is
- If patch object is given (patchA & patchB), the path is clipped to avoid the patch.
- The path is further shrunk by given amount of pixels (shrinkA & shrinkB)
- The path is transmuted to arrow patch, which is controlled by the
The creation of the connecting path between two points is controlled by
connectionstyle key and the following styles are available.
Note that "3" in
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).
The connecting path (after clipping and shrinking) is then mutated to
an arrow patch, according to the given
Some arrowstyles only work with connection styles that generate a
quadratic-spline segment. They are
For these arrow styles, you must use the "angle3" or "arc3" connection
If the annotation string is given, the patchA is set to the bbox patch of the text by default.
As in the text command, a box around the text can be drawn using
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.
# 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
mpl_toolkits.axes_grid1.anchored_artists others in
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)
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.
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.
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).
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.
# Using ConnectionPatch
The ConnectionPatch is like an annotation without text. While the annotate function is recommended in most situations, the 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, xyB=xy, coordsA="data", coordsB="data", axesA=ax1, axesB=ax2) ax2.add_artist(con)
The above code connects point xy in the data coordinates of
point xy in the data coordinates of
ax2. Here is a simple example.
While the ConnectionPatch instance can be added to any axes, you may want to add it to the axes that is latest in drawing order to prevent overlap by other 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 how mpl's transform works. But, utilizing it will be
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.
However, it is recommended that you derive from the matplotlib.patches.BoxStyle._Base as demonstrated below.
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.