问题描述
我有一个 4-D NumPy 数组，轴表示 x、y、z、t.我想取对应于 t=0 的切片并在 y 轴上排列顺序.
I have a 4-D NumPy array, with axis say x,y,z,t. I want to take slice corresponding to t=0 and to permute the order in the y axis.
我有以下内容
import numpy as np
a = np.arange(120).reshape(4,5,3,2)
b = a[:,[1,2,3,4,0],:,0]
b.shape
我得到 (5, 4, 3) 而不是 (4,5,3).
I get (5, 4, 3) instead of (4,5,3).
当我输入时
aa = a[:,:,:,0]
bb = aa[:,[1,2,3,4,0],:]
bb.shape
我得到了预期的 (4,5,3).有人能解释一下为什么第一个版本会交换前两个维度吗?
I get the expected (4,5,3). Can someone explain why does the first version swap the first two dimensions?
推荐答案
正如@hpaulj 在评论中提到的，这种行为是因为混合了基本切片和高级索引:
As @hpaulj mentioned in the comments, this behaviour is because of mixing basic slicing and advanced indexing:
a = np.arange(120).reshape(4,5,3,2)
b = a[:,[1,2,3,4,0],:,0]
在上面的代码片段中，发生的事情如下:
In the above code snippet, what happens is the following:
- 当我们沿最后一个维度进行基本切片时，它会触发一个
__getitem__调用.所以，那个维度消失了.(即没有单一维度) [1,2,3,4,0]从第二维返回 5 个切片.将此形状放入返回数组中有两种可能性:在第一个位置或在最后一个位置.NumPy 决定把它放在第一维.这就是为什么在返回的形状元组的第一个位置得到 5 (5, ...) 的原因.如果我没记错的话，Jaime 在 PyCon 的一次演讲中解释了这一点.
- when we do basic slicing along last dimension, it triggers a
__getitem__call. So, that dimension is gone. (i.e. no singleton dimension) [1,2,3,4,0]returns 5 slices from second dimension. There are two possibilities to put this shape in the returned array: either at the first or at the last position. NumPy decided to put it at the first dimension. This is why you get 5 (5, ...) in the first position in the returned shape tuple. Jaime explained this in one of the PyCon talks, if I recall correctly.
沿第一维和第三维，由于您使用 : 对所有内容进行切片，因此保留沿这些维度的原始长度.
Along first and third dimension, since you slice everything using :, the original length along those dimensions is retained.
将所有这些放在一起，NumPy 返回形状元组为:(5, 4, 3)
Putting all these together, NumPy returns the shape tuple as: (5, 4, 3)
您可以在 numpy-indexing-ambiguity-in-3d-阅读更多相关信息数组和arrays.indexing#combining-advanced-and-basic-indexing
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