"""
===============
Axes box aspect
===============

This demo shows how to set the aspect of an Axes box directly via
`~.Axes.set_box_aspect`. The box aspect is the ratio between Axes height
and Axes width in physical units, independent of the data limits.
This is useful to e.g. produce a square plot, 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.

The following lists a few use cases for `~.Axes.set_box_aspect`.
"""

# %%
# A square Axes, independent of data
# ----------------------------------
#
# 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
# ------------------
#
# 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
# ----------------
#
# 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. `~.Axes.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()

# %%
# Set data aspect with box aspect
# -------------------------------
#
# When setting the box aspect, one may still set the data aspect as well.
# Here we create an Axes with a box twice as long as it is tall and use
# an "equal" data aspect for its contents, i.e. the circle actually
# stays circular.

fig6, ax = plt.subplots()

ax.add_patch(plt.Circle((5, 3), 1))
ax.set_aspect("equal", adjustable="datalim")
ax.set_box_aspect(0.5)
ax.autoscale()

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()

# %%
#
# .. admonition:: References
#
#    The use of the following functions, methods, classes and modules is shown
#    in this example:
#
#    - `matplotlib.axes.Axes.set_box_aspect`
