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Legend guide addition #24707

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9d1ae91
Add example to legend guide
Dec 12, 2022
f7be69c
Add example to legend guide
Dec 12, 2022
57beaef
Merge branch 'legend-guide-addition' of github.com:matthewelmer/matpl…
Dec 12, 2022
f1ddbf1
Added a line
Dec 12, 2022
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45 changes: 45 additions & 0 deletions tutorials/intermediate/legend_guide.py
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Expand Up @@ -137,6 +137,51 @@

plt.show()

###############################################################################
# If you have multiple plots side-by-side each with a common legend and wish to
# instead have one legend that represents all subplots, you can do this:

import numpy as np
import matplotlib.pyplot as plt

# Make a mosaic of subplots and use the top one for the legend.
fig, axes = plt.subplot_mosaic(
(("legend", "legend", "legend"), # Top row has one column.
("f", "df_deta", "d2f_deta2")), # Bottom row has three columns.
height_ratios=(0.1, 0.9), # Size the dummy plot appropriately.
num="distributions") # I like to name my figures. :)
fig.set_constrained_layout(True)

eta = np.linspace(0, 1)
cap_lambda_vals = np.array([-24, -12, 0, 12, 24])
for cap_lambda in cap_lambda_vals:
# Some data
f = 2 * eta - 2 * eta**3 + eta**4 + cap_lambda * eta / 6 * (1 - eta)**3
df_deta = 2 - 6 * eta**2 + 4 * eta**3 + cap_lambda / \
6 * (1 - 6 * eta + 9 * eta**2 - 4 * eta**3)
d2f_deta2 = -12 * eta + 12 * eta**2 + \
cap_lambda / 6 * (-6 + 18 * eta - 12 * eta**2)

for axis in axes:
# Make the dummy plot blank.
if axis == "legend":
axes[axis].axis("off")
continue

# Plot data
axes["f"].plot(f, eta, label=f"$\Lambda = {cap_lambda}$")
axes["df_deta"].plot(df_deta, eta, label=f"$\Lambda = {cap_lambda}$")
axes["d2f_deta2"].plot(d2f_deta2, eta, label=f"$\Lambda = {cap_lambda}$")

# Add a legend to the dummy plot, using the handles from the plot you want to
# create the legend for.
axes["legend"].legend(
handles=axes["f"].get_legend_handles_labels()[0],
loc="center", ncol=len(cap_lambda_vals),
mode="expand", borderaxespad=0)

plt.show() # Ta-daa! (They look better if you squish em down a bit)

###############################################################################
# Multiple legends on the same Axes
# =================================
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