Python 通过索引列表对3D numpy数组进行切片
我看不出如何对数组进行切片,以便获得三维感兴趣的索引。下面是一个3D numpy阵列示例Python 通过索引列表对3D numpy数组进行切片,python,numpy,Python,Numpy,我看不出如何对数组进行切片,以便获得三维感兴趣的索引。下面是一个3D numpy阵列示例 data = np.arange(60).reshape(5,4,3) print data [[[ 0 1 2] [ 3 4 5] [ 6 7 8] [ 9 10 11]] [[12 13 14] [15 16 17] [18 19 20] [21 22 23]] [[24 25 26] [27 28 29] [30 31 32] [33 34
data = np.arange(60).reshape(5,4,3)
print data
[[[ 0 1 2] [ 3 4 5] [ 6 7 8] [ 9 10 11]]
[[12 13 14] [15 16 17] [18 19 20] [21 22 23]]
[[24 25 26] [27 28 29] [30 31 32] [33 34 35]]
[[36 37 38] [39 40 41] [42 43 44] [45 46 47]]
[[48 49 50] [51 52 53] [54 55 56] [57 58 59]]]
下面是我想从三维空间中获取的索引
感兴趣的指数=np.random.randint(3,大小=5)
打印感兴趣的索引
[0 2 2 2 0]
所以基本上我想要价值观
[[[ 0] [ 3] [ 6] [ 9]]
[[14] [17] [20] [23]]
[[26] [29] [32] [35]]
[[38] [41] [44] [47]]
[[48] [51] [54] [57]]]
有没有办法做到这一点?当我尝试直接为数组编制索引时,它会广播维度,而不是向我提供数据的子集。我们可以使用第三个维度来获取它们-
data[np.arange(len(indices_of_interest)),:, indices_of_interest]
样本运行-
In [65]: data = np.arange(60).reshape(5,4,3)
In [66]: indices_of_interest = [0,2,2,2,0]
In [67]: data[np.arange(len(indices_of_interest)),:, indices_of_interest]
Out[67]:
array([[ 0, 3, 6, 9],
[14, 17, 20, 23],
[26, 29, 32, 35],
[38, 41, 44, 47],
[48, 51, 54, 57]])