Python相当于Matlab的surfnorm

import numpy as np
from scipy.interpolate import griddata

def surfnorm(surface_points, x, y):
    """
    Calculate the surface normal vectors for a given set of points on a surface.
    
    Parameters:
    surface_points (np.array): A 2D array where each row is a point (x, y, z) on the surface.
    x (np.array): 1D array of x-coordinates for the grid.
    y (np.array): 1D array of y-coordinates for the grid.
    
    Returns:
    normals (np.array): A 2D array where each row is a normal vector (nx, ny, nz).
    """
    # Create a grid of points
    xi = np.linspace(x.min(), x.max(), len(x))
    yi = np.linspace(y.min(), y.max(), len(y))
    xi, yi = np.meshgrid(xi, yi)
    
    # Interpolate the z-values
    zi = griddata(surface_points[:, :2], surface_points[:, 2], (xi, yi), method='cubic')
    
    # Calculate the gradients
    dz_dx, dz_dy = np.gradient(zi)
    
    # Calculate the normal vectors
    nx = -dz_dx / np.sqrt(dz_dx**2 + dz_dy**2 + 1)
    ny = -dz_dy / np.sqrt(dz_dx**2 + dz_dy**2 + 1)
    nz = np.ones_like(nx)
    
    normals = np.column_stack((nx.flatten(), ny.flatten(), nz.flatten()))
    return normals

# Example usage
surface_points = np.array([
    [0, 0, 0],
    [1, 0, 1],
    [0, 1, 1],
    [1, 1, 0]
])

x = np.linspace(0, 1, 10)
y = np.linspace(0, 1, 10)

normals = surfnorm(surface_points, x, y)
print(normals)