本文实例为大家分享了python matlibplot绘制3d图形的具体代码，供大家参考，具体内容如下
1、散点图使用scatter
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from mpl_toolkits.mplot3d import axes3d
import numpy as np
from matplotlib import pyplot as plt
# 生成3d示例数据
mu_vec1 = np.array([0,0,0]) # 均值向量
cov_mat1 = np.array([[1,0,0],[0,1,0],[0,0,1]]) # 协方差矩阵
class1_sample = np.random.multivariate_normal(mu_vec1, cov_mat1, 20)
class2_sample = np.random.multivariate_normal(mu_vec1 + 1, cov_mat1, 20)
class3_sample = np.random.multivariate_normal(mu_vec1 + 2, cov_mat1, 20)
# class1_sample.shape -> (20, 3), 20 rows, 3 columns
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(class1_sample[:,0], class1_sample[:,1], class1_sample[:,2],
marker='x', color='blue', s=40, label='class 1')
ax.scatter(class2_sample[:,0], class2_sample[:,1], class2_sample[:,2],
marker='o', color='green', s=40, label='class 2')
ax.scatter(class3_sample[:,0], class3_sample[:,1], class3_sample[:,2],
marker='^', color='red', s=40, label='class 3')
ax.set_xlabel('variable x')
ax.set_ylabel('variable y')
ax.set_zlabel('variable z')
plt.title('3d scatter plot')
plt.show()
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2、直线使用plot3d
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from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
from itertools import product, combinations
fig = plt.figure(figsize=(7,7))
ax = fig.gca(projection='3d')
ax.set_aspect("equal")
# 画点
# 立方体里的点
x_inside = np.array([[0,0,0],[0.2,0.2,0.2],[0.1, -0.1, -0.3]])
x_outside = np.array([[-1.2,0.3,-0.3],[0.8,-0.82,-0.9],[1, 0.6, -0.7],
[0.8,0.7,0.2],[0.7,-0.8,-0.45],[-0.3, 0.6, 0.9],
[0.7,-0.6,-0.8]])
for row in x_inside:
ax.scatter(row[0], row[1], row[2], color="r", s=50, marker='^')
for row in x_outside:
ax.scatter(row[0], row[1], row[2], color="k", s=50)
# 画立方体
h = [-0.5, 0.5]
for s, e in combinations(np.array(list(product(h,h,h))), 2):
if np.sum(np.abs(s-e)) == h[1]-h[0]:
ax.plot3d(*zip(s,e), color="g")
ax.set_xlim(-1.5, 1.5)
ax.set_ylim(-1.5, 1.5)
ax.set_zlim(-1.5, 1.5)
plt.show()
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以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/yewuqingxuan/article/details/74732945