Introduction
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.
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Head shape fixed in display space, anchor points fixed in data 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.
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
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
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))
plt.show()
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.
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))
plt.show()
Entire patch fixed in data space
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))
plt.show()
Summary
This tutorial showed how to plot arrows that behave differently when the data limits on a plot are changed. It covered three use cases for plotting arrows, depending on whether the head or anchor points need to be fixed in data or display space. These use cases can be useful for annotating plots and ensuring that the arrow stays in the correct position even if the plot is panned or scaled.
🚀 Practice Now: Matplotlib Arrow Guide
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