适当的numpy矢量化
我正在尝试矢量化我的代码。我正在转化吗适当的numpy矢量化,numpy,Numpy,我正在尝试矢量化我的代码。我正在转化吗 def acceleration_1(grid): nx = grid.shape[0] ny = grid.shape[1] acc = np.zeros((nx,ny,2)) for i in range(1,nx-1): for j in range(1,ny-1): acc[i,j,0] = grid[i+1,j,0] + grid[i-1,j,0] - 2*grid[i,j,0] acc[i,j,
def acceleration_1(grid):
nx = grid.shape[0]
ny = grid.shape[1]
acc = np.zeros((nx,ny,2))
for i in range(1,nx-1):
for j in range(1,ny-1):
acc[i,j,0] = grid[i+1,j,0] + grid[i-1,j,0] - 2*grid[i,j,0]
acc[i,j,1] = grid[i,j+1,1] + grid[i,j-1,1] - 2*grid[i,j,1]
def acceleration_2(grid):
nx = np.arange(1,grid.shape[0]-1)
ny = np.arange(1,grid.shape[1]-1)
acc = np.zeros((grid.shape[0],grid.shape[0],2))
acc[nx,ny,0] = grid[nx+1,ny,0] + grid[nx-1,ny,0] - 2*grid[nx,ny,0]
acc[nx,ny,1] = grid[nx,ny+1,1] + grid[nx,ny-1,1] - 2*grid[nx,ny,1]
我知道我也可以用矩阵乘法来表示。但将其转换为矩阵运算似乎很麻烦。通过将for循环转换为“nx”和“ny”上的隐式迭代，我是否可以获得最佳加速 好像你在做卷积运算。你可以用numpy来做这件事，就像你展示的那样。或者，我们可以使用内置卷积功能：
from scipy.signal import convolve2d
k_y = np.array([[1, -2, 1]]).T
k_x = np.array([[1, -2, 1]])
acc = np.zeros_like(grid)
acc[:, :, 0] = convolve2d(grid[:, :, 0], k_y, mode='same')
acc[:, :, 1] = convolve2d(grid[:, :, 1], k_x, mode='same')
要完全用numpy来实现这一点：
pad_y = np.pad(grid[:, :, 0], ((1, 1), (0, 0)), mode='constant')
pad_x = np.pad(grid[:, :, 1], ((0, 0), (1, 1)), mode='constant')
up = pad_y[:-2, 1:-1]
down = pad_y[2:, 1:-1]
left = pad_x[1:-1, :-2]
right = pad_x[1:-1, 2:]
acc = np.zeros_like(grid)
acc[:, :, 0] = up + down - 2 * grid[:, :, 0]
acc[:, :, 1] = left + right - 2 * grid[:, :, 1]
第一个例子不起作用。为什么不
[i，j，0][i，j，1]grid[i，j+1,1]grid[i，j+1,2]acc_2