创建工程数据的3D可视化有助于理解复杂的结构和行为. 将SciPy与Matplotlib整合,可以进行高级数据分析和可视化能力. 本文解释了如何有效地使用这些工具生成3D图.

建立环境

要开始, 如果必要的库尚未可用, 则安装它们。 使用 pip 安装 SciPy 和 Matplotlib :

命令:]

pip 安装 scipy matplotlib

准备数据可视化

使用 SciPy 函数或其他数据源生成或加载工程数据。例如,为表层绘图创建网格:

实例:]

导入数字为 np

x = np.linspace(5,5,100) = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

y = np.linspace(5,5,100) = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

X, Y = np.meshgrid(x, y) 数据

Z = np.sin(np.sqrt(+++2 + Y**2))) ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇

创建 3D 视觉

使用 Matplotlib 的 3D 绘图能力来可视化数据。 导入 3D 工具包并设置图 :

实例:]

导入 matplotlib.pyplot 作为 plt

从 mpl toolkits.mplot3d 导入轴3D

fig = plt. figure () 数字

ax = fig.add subplot(111,投影='3d') 互联网档案馆的存檔,存档日期2013-03-02.

ax.plot 表面(X, Y, Z, cmap='viridis') 互联网档案馆的存檔,存档日期2013-09-02.

plt.show () 数据

额外的可视化提示

调整颜色图、添加标签和自定义视图以提升清晰度。为不同的视角使用不同的图案功能,如线框或轮廓图。

定制化的例子包括:

  • 变化的颜色图:[] cmap='plasma'
  • 添加标签: ax.set xlabel('X轴')
  • 调整视图角度: ax.view init(elev=30,方位角=45)