numpy meshgrid 和 mgrid 的两个简单实例和解析

numpy.meshgrid 和 numpy.mgrid 用于返回包含坐标向量的坐标矩阵. 当坐标矩阵为二维时, 可用于在图像变形时构建网格. 

 

实例一

from __future__ import print_function import numpy as npgrid_y1, grid_x1 = np.meshgrid(range(5), range(3)) grid_x2, grid_y2 = np.mgrid[0:3, 0:5]# Two arrays are element-wise equal within a tolerance. print ("grid_x1 == grid_x2?", np.allclose(grid_x1, grid_x2)) # True. print ("grid_y1 == grid_y2?", np.allclose(grid_y2, grid_y2)) # True.

注意, 对于 np.meshgrid(range(5), range(3)), 

* 返回两个数组 grid_y1和grid_x1，形状均为 3 x 5, 不是 5 x 3 ; 
* 返回的第一个数组元素来自 range(5)，即 3 行，每行均为 [0, 1, 2, 3, 4] ;
* 返回的第二个数组元素来自 range(3), 即 5 列，每列均为[0,1,2]

 

实例二

from __future__ import print_function import numpy as npgrid_y1, grid_x1 = np.meshgrid(np.linspace(0,1,200), np.linspace(0,1,100)) # output 100 x 200 array grid_x2, grid_y2 = np.mgrid[0:1:100j, 0:1:200j] # output 100 x 200 array # Two arrays are element-wise equal within a tolerance. print ("grid_x1 == grid_x2?", np.allclose(grid_x1, grid_x2)) # True. print ("grid_y1 == grid_y2?", np.allclose(grid_y2, grid_y2)) # True.

注:

grid_y1, grid_x1 均为 100 x 200 数组.

grid_y1 数组有 100 行, 每行均为 np.linspace(0,1,200), 与 grid_y2 相同 ;

grid_x1 数组有 200 列, 每列均为 np.linspace(0,1,100), 与 grid_x2 相同 ;

0:1:100j 索引表示包含两端即 0 和 1 , 均分为 100 个点 , 与 np.linspace(0,1,100) 含义相同.

 

转载于:https://www.cnblogs.com/klchang/p/10633972.html

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