import matplotlib.pyplot as plt import numpy as np fig = plt.figure() ax = fig.gca(projection='3d') X = np.arange(-5, 5, 0.25) Y = np.arange(-5, 5, 0.25) X, Y = np.meshgrid(X, Y) R = np.sqrt(X**2 + Y**2) Z = np.sin(R) surf = ax.plot_surface(X, Y, Z,...
ax.plot(x, y, z, label='parametric curve') ax.legend() plt.show() (9) 4D plot from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt import numpy as np fig = plt.figure() ax = fig.add_subplot(111, projection='3d') x = np.random.standard_normal(100) y = n...
示例:numpy_stl_example_02.ipynb import numpy as np from scipy import spatial from stl import mesh from myplot import plot_mesh vertices = np.array( [ [-3, -3, 0], [+3, -3, 0], [+3, +3, 0], [-3, +3, 0], [+0, +0, +3] ] ) hull = spatial.ConvexHull(vertices) fa...
import numpy as npimport matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import Axes3D# 创建数据x = np.linspace(0, 1, 100)y = np.sin(2 * np.pi * x)z = np.cos(2 * np.pi * x)# 创建图形和轴fig = plt.figure()ax = fig.add_subplot(111, projection='3d')# 绘制带标记的线...
使用matplotlib进行三维可视化需要使用其3D绘图功能,即mpl_toolkits.mplot3d。下面是一个简单的例子,展示如何使用matplotlib进行三维数据的可视化:```pythonimport matplotlib.pyplot as pltimport numpy as npfrom mpl_toolkits.mplot3d import Axes3D# 生成数据x = np.random.standard_normal(100)y = np.random....
其中就有我们需要参考的部分,也就是mplot3d example code : 2dcollections3d_demo.py。下面贴出其中的代码段。 """ === Plot 2D data on 3D plot === Demonstrates using ax.plot's zdir keyword to plot 2D data on selective axes of a 3D plot. """ from mpl_toolkits.mplot...
fig.add_subplot(``1``, ``2``, ``2``, projection``=``'3d'``)` `# plot a 3D wireframe like in the example mplot3d/wire3d_demo``X, Y, Z ``=` `get_test_data(``0.05``)``ax.plot_wireframe(X, Y, Z, rstride``=``10``, cstride``=``10``)` `plt.show()`...
ax = fig.add_subplot(2,1,2, projection='3d') # plot a 3D wireframe like in the example mplot3d/wire3d_demo X, Y, Z = get_test_data(0.05) ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10) plt.show() 补充: 文本注释的基本用法: code: 1 2 3 4 5 6 7 8 9 10 11 ...
3. 3D条形图(3D Bar Plot) 代码语言:javascript 代码运行次数:0 运行 AI代码解释 import matplotlib.pyplot as plt import numpy as np # 数据准备 x = np.arange(3) # x轴位置 y = np.arange(3) # y轴位置 x_mesh, y_mesh = np.meshgrid(x, y) # 创建网格 z = np.array([[1, 2, 3],...
The screenshot below is based on a Python script module creating a PyQt application. This application creates a scatter plot of measures stored in a label-analysis. Download this example Scatter plot of measures stored in a labe...