如何在 Python 中绘制 3D 曲面

Question

为了从以下数据生成 3D 表面，我应该在 here 和 here 的任何示例中更改/添加什么？

[100, 0, 'TF', 0]
[100, 0, 'DF', 0]
[100, 50, 'TF', 0]
[100, 50, 'DF', 0]
[100, 100, 'TF', 0]
[100, 100, 'DF', 0]
[100, 150, 'TF', 0.84496124031007758]
[100, 150, 'DF', 0.87209302325581395]
[100, 200, 'TF', 0.88139534883720927]
[100, 200, 'DF', 0.86201550387596892]
[100, 250, 'TF', 0.87441860465116272]
[100, 250, 'DF', 0.889922480620155]
[100, 300, 'TF', 0.87984496124031009]
[100, 300, 'DF', 0.89922480620155043]
[100, 350, 'TF', 0.92015503875968996]
[100, 350, 'DF', 0.90697674418604657]

注意：我确实根据上面提到的代码尝试了一些事情，但到目前为止我还没有设法让它工作。我没有在这里引用我的代码的原因是因为我会让我的问题变得比实际更复杂。

Answer 1

因此，您的数据并不是真正的 3D，因为所有 x 值都相同，但这是我编辑您发布的示例的方式。出于某种原因，X 想要成为列表列表。也许其他人知道为什么。

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

#step = 0.04
#maxval = 1.0
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# create supporting points in polar coordinates
#r = np.linspace(0,1.25,50)
#p = np.linspace(0,2*np.pi,50)
#R,P = np.meshgrid(r,p)
# transform them to cartesian system
#X,Y = R*np.cos(P),R*np.sin(P)

X=[]
for i in range(0,16):
    X.append([])
    X[i].append(100)
print(X)

Y=[]
for i in range(0,8):
    Y.append(50*i)
    Y.append(50*i)
print(Y)

Z=[]
Z.append(0)
Z.append(0)
Z.append(0)
Z.append(0)
Z.append(0)
Z.append(0)
Z.append(0.84496124031007758)
Z.append(0.87209302325581395)
Z.append(0.88139534883720927)
Z.append(0.86201550387596892)
Z.append(0.87441860465116272)
Z.append(0.889922480620155)
Z.append(0.87984496124031009)
Z.append(0.89922480620155043)
Z.append(0.92015503875968996)
Z.append(0.90697674418604657)
print(Z)


#Z = ((R**2 - 1)**2)
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.YlGnBu_r)
ax.set_zlim3d(0, 1)
ax.set_xlabel(r'xlabel')
ax.set_ylabel(r'ylabel')
ax.set_zlabel(r'zlabel')
plt.show()

Answer 2

对 x 数据稍作改动就会给你一个表面：

from mpl_toolkits.mplot3d import Axes3D
import matplotlib
import numpy as np
from matplotlib import cm
from matplotlib import pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

data = [[100, 0, 'TF', 0],
        [100, 0, 'DF', 0],
        [100, 50, 'TF', 0],
        [100, 50, 'DF', 0],
        [100, 100, 'TF', 0],
        [100, 100, 'DF', 0],
        [100, 150, 'TF', 0.84496124031007758],
        [100, 150, 'DF', 0.87209302325581395],
        [100, 200, 'TF', 0.88139534883720927],
        [100, 200, 'DF', 0.86201550387596892],
        [100, 250, 'TF', 0.87441860465116272],
        [100, 250, 'DF', 0.889922480620155],
        [100, 300, 'TF', 0.87984496124031009],
        [100, 300, 'DF', 0.89922480620155043],
        [100, 350, 'TF', 0.92015503875968996],
        [100, 350, 'DF', 0.90697674418604657]
        ]
x = [item[0] for item in data]
# change x here
x = np.linspace(0, 100, num = 16)
y = [item[1] for item in data]
z = [item[3] for item in data]

X, Y = np.meshgrid(x, y)
_z, Z = np.meshgrid(x, z)

ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.YlGnBu_r)
ax.set_zlim3d(0, 1)
ax.set_xlabel(r'xlabel')
ax.set_ylabel(r'ylabel')
ax.set_zlabel(r'zlabel')
plt.show()
plt.close()