Matplotlib - 强制3D图使用可用的figsize

Question

Matplotlib - 强制3D图使用可用的figsize

3

我想创建一个指定大小的图形，并使3D轴尽可能地利用所有可用空间，同时保持所有轴上的等比例。

我的当前尝试显示了一个剪切矩形，隐藏了部分网格。

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np

fig = plt.figure(figsize=(5, 2.5))
ax = fig.add_subplot(projection='3d')
ax.get_proj = lambda: np.dot(Axes3D.get_proj(ax), np.diag([1, 1, 1, 0.5]))

u = v = np.linspace(0, 2 * np.pi, 50)
u, v = np.meshgrid(u, v)
X = np.cos(v) * (6 - (5/4 + np.sin(3 * u)) * np.sin(u - 3 * v))
Y = (6 - (5/4 + np.sin(3 * u)) * np.sin(u - 3 * v)) * np.sin(v)
Z = -np.cos(u - 3 * v) * (5/4 + np.sin(3 * u))

ax.plot_surface(X, Y, Z, rstride=1, cstride=1, color=[0.7] * 3, linewidth=0.25, edgecolor="k")
ax.set_box_aspect([ub - lb for lb, ub in (getattr(ax, f'get_{a}lim')() for a in 'xyz')])

plt.show()

我该怎么做？

- Davide_sd

1个回答

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- Karina · Accepted Answer

我复制了您提供的代码，只是在最后面添加了4行代码。

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
plt.close()
fig = plt.figure(figsize=(5, 2.5))
ax = fig.add_subplot(projection='3d')
ax.get_proj = lambda: np.dot(Axes3D.get_proj(ax), np.diag([1, 1, 1, 0.5]))

u = v = np.linspace(0, 2 * np.pi, 50)
u, v = np.meshgrid(u, v)
X = np.cos(v) * (6 - (5/4 + np.sin(3 * u)) * np.sin(u - 3 * v))
Y = (6 - (5/4 + np.sin(3 * u)) * np.sin(u - 3 * v)) * np.sin(v)
Z = -np.cos(u - 3 * v) * (5/4 + np.sin(3 * u))

ax.plot_surface(X, Y, Z, rstride=1, cstride=1, color=[0.7] * 3, linewidth=0.25, edgecolor="k")
# ax.set_box_aspect([ub - lb for lb, ub in (getattr(ax, f'get_{a}lim')() for a in 'xyz')])


left, right = plt.xlim()
ax.set_zlim(left, right)
ax.set_ylim(left, right)
plt.tight_layout()

我注释掉了ax.set_box_aspect这行代码，因为它会报错。以上的输出结果是：

--- 编辑 ---

我有一个解决办法可以让它在matplotlib v3.4.2中正常工作：

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
import os

plt.close()

# your code starts here, with a little modification
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(projection='3d')

u = v = np.linspace(0, 2 * np.pi, 50)
u, v = np.meshgrid(u, v)
X = np.cos(v) * (6 - (5/4 + np.sin(3 * u)) * np.sin(u - 3 * v))
Y = (6 - (5/4 + np.sin(3 * u)) * np.sin(u - 3 * v)) * np.sin(v)
Z = -np.cos(u - 3 * v) * (5/4 + np.sin(3 * u))
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, color=[0.7] * 3, linewidth=0.25, edgecolor="k")

# set the axes limits
left, right = plt.xlim()
ax.set_zlim(left, right)
ax.set_ylim(left, right)

# zoom in to the plot
ax.dist = 6

# make everything other than the plot itself transparent
fig.patch.set_alpha(0)
ax.patch.set_alpha(0)
ax.axis('off')
plt.tight_layout()

# save plot as image
plt.savefig('plotted.png')

# remove the axes where the image was plotted
ax.remove()

# resize figsize
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(5, 2.5)

# add fake axes for gridlines as in 3d plot to make it look like a real plot
# skip this part if the gridlines are unnecessary
ax_bg = fig.add_subplot(111, projection='3d')
ax_bg.dist = 3

# add axes in cartesian coordinates (xy-plane) for the image
ax = fig.add_subplot(111)
fig.patch.set_alpha(1)
fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0)
im = plt.imread('plotted.png')

h, w, dc = im.shape # (height=500, width=500, depth/color=4)
im_cropped = im[120:390, :, :] # this is manually adjusted
ax.axis('off')
ax.imshow(im_cropped)

# delete the saved image
os.remove('plotted.png')

输出结果为：

我不知道它是否适用于您的特定情况，但它可以满足您的问题。

如果有不清楚的地方，请让我知道。