can np.meshgrid() be used to create 3D grids for visualizing high-dimensional data

1. Plotting 3D Functions:

python
   import numpy as np
   import matplotlib.pyplot as plt

   # Create a grid of x and y values
   x = np.linspace(-5, 5, 100)
   y = np.linspace(-5, 5, 100)
   xx, yy = np.meshgrid(x, y)

   # Calculate the function values
   z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2)

   # Create a 3D plot
   fig = plt.figure(figsize=(10, 14))
   ax = fig.gca(projection="3d")
   ax.plot_surface(xx, yy, z)
   plt.show()
   

2. Creating 3D Meshgrids:

python
   import numpy as np
   import matplotlib.pyplot as plt

   # Create a grid of x, y, and z values
   X = np.linspace(1, 4, 4)
   Y = np.linspace(6, 8, 3)
   Z = np.linspace(12, 15, 4)
   xx, yy, zz = np.meshgrid(X, Y, Z)

   # Visualize the 3D grid
   fig = plt.figure()
   ax = fig.add_subplot(111, projection='3d')
   ax.scatter(xx, yy, zz)
   ax.set_zlim(12, 15)
   plt.show()
   

3. Creating 3D Surface Plots:

python
   import numpy as np
   import matplotlib.pyplot as plt

   # Create a grid of x and y values
   X = np.linspace(-20, 20, 100)
   Y = np.linspace(-20, 20, 100)
   X, Y = np.meshgrid(X, Y)

   # Calculate the function values
   Z = 4 * X**2 + Y**2

   # Create a 3D surface plot
   fig = plt.figure()
   ax = fig.add_subplot(111, projection='3d')
   ax.plot_surface(X, Y, Z, cmap="plasma", linewidth=0, antialiased=False, alpha=0.5)
   plt.show()
   

These examples demonstrate how `np.meshgrid()` can be used to create 3D grids and visualize high-dimensional data.