Arrow guide

Adding arrow patches to plots.

Arrows are often used to annotate plots. This tutorial shows how to plot arrows that behave differently when the data limits on a plot are changed. In general, points on a plot can either be fixed in "data space" or "display space". Something plotted in data space moves when the data limits are altered - an example would be the points in a scatter plot. Something plotted in display space stays static when data limits are altered - an example would be a figure title or the axis labels.

Arrows consist of a head (and possibly a tail) and a stem drawn between a start point and end point, called 'anchor points' from now on. Here we show three use cases for plotting arrows, depending on whether the head or anchor points need to be fixed in data or display space:

1. Head shape fixed in display space, anchor points fixed in data space

2. Head shape and anchor points fixed in display space

3. Entire patch fixed in data space

Below each use case is presented in turn.

import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
x_tail = 0.1
y_tail = 0.5
x_head = 0.9
y_head = 0.8
dx = x_head - x_tail
dy = y_head - y_tail

Head shape fixed in display space and anchor points fixed in data space

This is useful if you are annotating a plot, and don't want the arrow to to change shape or position if you pan or scale the plot.

In this case we use patches.FancyArrowPatch.

Note that when the axis limits are changed, the arrow shape stays the same, but the anchor points move.

fig, axs = plt.subplots(nrows=2)
arrow = mpatches.FancyArrowPatch((x_tail, y_tail), (x_head, y_head),
                                 mutation_scale=100)
axs[0].add_patch(arrow)

arrow = mpatches.FancyArrowPatch((x_tail, y_tail), (x_head, y_head),
                                 mutation_scale=100)
axs[1].add_patch(arrow)
axs[1].set(xlim=(0, 2), ylim=(0, 2))

[(0.0, 2.0), (0.0, 2.0)]

Head shape and anchor points fixed in display space

This is useful if you are annotating a plot, and don't want the arrow to change shape or position if you pan or scale the plot.

In this case we use patches.FancyArrowPatch, and pass the keyword argument transform=ax.transAxes where ax is the axes we are adding the patch to.

Note that when the axis limits are changed, the arrow shape and location stay the same.

fig, axs = plt.subplots(nrows=2)
arrow = mpatches.FancyArrowPatch((x_tail, y_tail), (x_head, y_head),
                                 mutation_scale=100,
                                 transform=axs[0].transAxes)
axs[0].add_patch(arrow)

arrow = mpatches.FancyArrowPatch((x_tail, y_tail), (x_head, y_head),
                                 mutation_scale=100,
                                 transform=axs[1].transAxes)
axs[1].add_patch(arrow)
axs[1].set(xlim=(0, 2), ylim=(0, 2))

[(0.0, 2.0), (0.0, 2.0)]

Head shape and anchor points fixed in data space

In this case we use patches.Arrow, or patches.FancyArrow (the latter is in orange).

Note that when the axis limits are changed, the arrow shape and location change.

FancyArrow's API is relatively awkward, and requires in particular passing length_includes_head=True so that the arrow tip is (dx, dy) away from the arrow start. It is only included in this reference because it is the arrow class returned by Axes.arrow (in green).

fig, axs = plt.subplots(nrows=2)

arrow = mpatches.Arrow(x_tail, y_tail, dx, dy)
axs[0].add_patch(arrow)
arrow = mpatches.FancyArrow(x_tail, y_tail - .4, dx, dy,
                            width=.1, length_includes_head=True, color="C1")
axs[0].add_patch(arrow)
axs[0].arrow(x_tail + 1, y_tail - .4, dx, dy,
             width=.1, length_includes_head=True, color="C2")

arrow = mpatches.Arrow(x_tail, y_tail, dx, dy)
axs[1].add_patch(arrow)
arrow = mpatches.FancyArrow(x_tail, y_tail - .4, dx, dy,
                            width=.1, length_includes_head=True, color="C1")
axs[1].add_patch(arrow)
axs[1].arrow(x_tail + 1, y_tail - .4, dx, dy,
             width=.1, length_includes_head=True, color="C2")
axs[1].set(xlim=(0, 2), ylim=(0, 2))

[(0.0, 2.0), (0.0, 2.0)]

plt.show()