这是一个基于初始化的方法-
def create_grid(m,n): out = np.empty((m,n,2),dtype=int) #Improvement suggested by @AndrasDeak out[...,0] = np.arange(m)[:,None] out[...,1] = np.arange(n) return out
样品运行-
In [47]: create_grid(4,5)Out[47]: array([[[0, 0], [0, 1], [0, 2], [0, 3], [0, 4]], [[1, 0], [1, 1], [1, 2], [1, 3], [1, 4]], [[2, 0], [2, 1], [2, 2], [2, 3], [2, 4]], [[3, 0], [3, 1], [3, 2], [3, 3], [3, 4]]])
到目前为止针对
(4,5)网格化和更大尺寸发布的所有方法的运行时测试-
In [111]: %timeit np.moveaxis(np.indices((4,5)), 0, -1) ...: %timeit np.mgrid[:4, :5].swapaxes(2, 0).swapaxes(0, 1) ...: %timeit np.mgrid[:4,:5].transpose(1,2,0) ...: %timeit create_grid(4,5) ...: 100000 loops, best of 3: 11.1 µs per loop100000 loops, best of 3: 17.1 µs per loop100000 loops, best of 3: 17 µs per loop100000 loops, best of 3: 2.51 µs per loopIn [113]: %timeit np.moveaxis(np.indices((400,500)), 0, -1) ...: %timeit np.mgrid[:400, :500].swapaxes(2, 0).swapaxes(0, 1) ...: %timeit np.mgrid[:400,:500].transpose(1,2,0) ...: %timeit create_grid(400,500) ...: 1000 loops, best of 3: 351 µs per loop1000 loops, best of 3: 1.01 ms per loop1000 loops, best of 3: 1.03 ms per loop10000 loops, best of 3: 190 µs per loop
