Question

我有一个3d NumPy数组，我想从中获取很多切片。这些切片在第一和第二维度上具有一个或多个的长度，而第三个切片将完整地返回。切片应始终为3d。

我的尝试：

import numpy as np

a = np.zeros((1000, 10, 100))
row_sets = ([19, 20], [21])
col_sets = ([6], [7, 8])

for rows in row_sets:
    for cols in col_sets:
        b = a[[rows], [cols]]
        print(rows, cols, b.shape)

结果：

[19, 20] [6] (1, 2, 100)
[19, 20] [7, 8] (1, 2, 100)
[21] [6] (1, 1, 100)
[21] [7, 8] (1, 2, 100)

如果我从切片中删除嵌套括号：

b = a[rows, cols]

我在第二维中看起来是同一个问题，并且不保留维度：

[19, 20] [6] (2, 100)
[19, 20] [7, 8] (2, 100)
[21] [6] (1, 100)
[21] [7, 8] (2, 100)

我要找的结果是这样的：

[19, 20] [6] (2, 1, 100)
[19, 20] [7, 8] (2, 2, 100)
[21] [6] (1, 1, 100)
[21] [7, 8] (1, 2, 100)

Answer 1

通过使用整数列表作为索引来触发 advanced indexing ，这会减少结果数组的维数，如果要对数组进行切片，可以使用{{ 3}}从int列表中重建切片索引：

for rows in row_sets:
    for cols in col_sets:
        b = a[np.ix_(rows, cols)]
        print(rows, cols, b.shape)

#[19, 20] [6] (2, 1, 100)
#[19, 20] [7, 8] (2, 2, 100)
#[21] [6] (1, 1, 100)
#[21] [7, 8] (1, 2, 100)

使用可变NumPy切片

1 个答案: