如何创建可以使 matplotlib 3d 中的球体看起来像一半亮的颜色图图（看起来像地球的一侧被照亮）

Posted 2023-03-27

【中文标题】如何创建可以使 matplotlib 3d 中的球体看起来像一半亮的颜色图图（看起来像地球的一侧被照亮）

【英文标题】：How do I create a colormap plot that can make a sphere in matplotlib 3d look like half i bright (look like the earth is illuminated on one side)

【发布时间】：2019-10-28 15:44:27

【问题描述】：

我正在尝试在 matplotlib 中创建一个 3D 球体，并让它的颜色就像球体的一侧被太阳照亮一样。

我尝试过使用 matplotlib 颜色图。

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

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.set_axis_off()
phi = np.linspace(0,2*np.pi, 50)
theta = np.linspace(0, np.pi, 25)
x=np.outer(np.cos(phi), np.sin(theta))
y=np.outer(np.sin(phi), np.sin(theta))
z=np.outer(np.ones(np.size(phi)), np.cos(theta))
PHI=np.outer(phi,np.ones(np.size(theta)))
THETA=np.outer(np.ones(np.size(phi)),theta)
data = PHI/np.pi
norm = plt.Normalize(vmin=data.min(), vmax=data.max()) 
surface=ax.plot_surface(x, y, z, cstride=1, rstride=1, 
                        facecolors=cm.jet(norm(data)))
surface=ax.plot_surface(x, y, z, cstride=1, rstride=1, 
                        facecolors=cm.binary(norm(data)),cmap=plt.get_cmap('jet'))
plt.show()

我期待一个看起来像这样的球体： Or basically something that looks like the earth with the day side and the night side

但我的结果是这样的： current plot from the above code

【参考方案1】：

你需要使用LightSource包：

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

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.set_axis_off()
phi = np.linspace(0,2*np.pi, 100)
theta = np.linspace(0, np.pi, 50)
x=np.outer(np.cos(phi), np.sin(theta))
y=np.outer(np.sin(phi), np.sin(theta))
z=np.outer(np.ones(np.size(phi)), np.cos(theta))
PHI=np.outer(phi,np.ones(np.size(theta)))
THETA=np.outer(np.ones(np.size(phi)),theta)
data = PHI/np.pi
norm = plt.Normalize(vmin=data.min(), vmax=data.max())


# use Light Source
ls = LightSource(0, 0)
# create rgb shade
rgb = ls.shade(x, cmap=cm.Wistia, vert_exag=0.1, blend_mode='soft')
# blend shade
bsl = ls.blend_hsv(rgb, np.expand_dims(x*0.8, 2))
# plot surface
surface = ax.plot_surface(x, y, z, cstride=1, rstride=1, facecolors=bsl,
                       linewidth=0, antialiased=False, shade=False)


plt.show()

输出：