Matplotlib Box Aspect: Creating Square Plots with Ease

Introduction

This lab will guide you through the process of creating different plots using the set_box_aspect() method in Matplotlib. This method sets the aspect ratio between axes height and width in physical units, independent of data limits. It is useful for producing square plots, independent of the data it contains, or to have a usual plot with the same axes dimensions next to an image plot with fixed (data-)aspect.

VM Tips

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Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

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A Square Axes, Independent of Data

We will produce a square axes, no matter what the data limits are.

import matplotlib.pyplot as plt
import numpy as np

fig1, ax = plt.subplots()

ax.set_xlim(300, 400)
ax.set_box_aspect(1)

plt.show()

Shared Square Axes

We will produce shared subplots that are squared in size.

fig2, (ax, ax2) = plt.subplots(ncols=2, sharey=True)

ax.plot([1, 5], [0, 10])
ax2.plot([100, 500], [10, 15])

ax.set_box_aspect(1)
ax2.set_box_aspect(1)

plt.show()

Square Twin Axes

We will produce a square axes, with a twin axes. The twinned axes takes over the box aspect of the parent.

fig3, ax = plt.subplots()

ax2 = ax.twinx()

ax.plot([0, 10])
ax2.plot([12, 10])

ax.set_box_aspect(1)

plt.show()

Normal Plot Next to Image

When creating an image plot with fixed data aspect and the default adjustable="box" next to a normal plot, the axes would be unequal in height. set_box_aspect() provides an easy solution to that by allowing to have the normal plot's axes use the images dimensions as box aspect. This example also shows that constrained layout interplays nicely with a fixed box aspect.

fig4, (ax, ax2) = plt.subplots(ncols=2, layout="constrained")

np.random.seed(19680801)  ## Fixing random state for reproducibility
im = np.random.rand(16, 27)
ax.imshow(im)

ax2.plot([23, 45])
ax2.set_box_aspect(im.shape[0]/im.shape[1])

plt.show()

Square Joint/Marginal Plot

It may be desirable to show marginal distributions next to a plot of joint data. The following creates a square plot with the box aspect of the marginal axes being equal to the width- and height-ratios of the gridspec. This ensures that all axes align perfectly, independent on the size of the figure.

fig5, axs = plt.subplots(2, 2, sharex="col", sharey="row",
                         gridspec_kw=dict(height_ratios=[1, 3],
                                          width_ratios=[3, 1]))
axs[0, 1].set_visible(False)
axs[0, 0].set_box_aspect(1/3)
axs[1, 0].set_box_aspect(1)
axs[1, 1].set_box_aspect(3/1)

np.random.seed(19680801)  ## Fixing random state for reproducibility
x, y = np.random.randn(2, 400) * [[.5], [180]]
axs[1, 0].scatter(x, y)
axs[0, 0].hist(x)
axs[1, 1].hist(y, orientation="horizontal")

plt.show()

Box Aspect for Many Subplots

It is possible to pass the box aspect to an Axes at initialization. The following creates a 2 by 3 subplot grid with all square Axes.

fig7, axs = plt.subplots(2, 3, subplot_kw=dict(box_aspect=1),
                         sharex=True, sharey=True, layout="constrained")

for i, ax in enumerate(axs.flat):
    ax.scatter(i % 3, -((i // 3) - 0.5)*200, c=[plt.cm.hsv(i / 6)], s=300)
plt.show()

Summary

This lab provided an overview of how to use set_box_aspect() in Matplotlib to create different types of plots with a fixed aspect ratio between axes height and width.

Matplotlib Box Aspect