希望有人可以向我解释以下我用numpy数组观察到的行为：

>>> import numpy as np
>>> data_block=np.zeros((26,480,1000))
>>> indices=np.arange(1000)
>>> indices.shape
(1000,)
>>> data_block[0,:,:].shape
(480, 1000)            #fine and dandy
>>> data_block[0,:,indices].shape
(1000, 480)            #what happened????  why the transpose????
>>> ind_slice=np.arange(300)    # this is more what I really want.
>>> data_block[0,:,ind_slice].shape
(300, 480)     # transpose again!   arghhh!

我不了解这种转换行为,这对我想做的事情非常不方便.有人可以向我解释吗？获得data_block子集的另一种方法将是很大的收获.

解决方法:

您可以通过以下方式获得所需的结果：

>>> data_block[0,:,:][:,ind_slice].shape
(480L, 300L)

我承认我对numpy索引的工作原理没有完全的了解,但是the documentation似乎暗示了您遇到的麻烦：

Basic slicing with more than one non-: entry in the slicing tuple, acts like repeated application of slicing using a single non-: entry, where the non-: entries are successively taken (with all other non-: entries replaced by :). Thus, x[ind1,...,ind2,:] acts like x[ind1][...,ind2,:] under basic slicing.

Warning: The above is not true for advanced slicing.

和. . .

Advanced indexing is triggered when the selection object, obj, is a non-tuple sequence object, an ndarray (of data type integer or bool), or a tuple with at least one sequence object or ndarray (of data type integer or bool).

因此,您通过使用ind_slice数组而不是常规切片进行索引来触发该行为.

文档本身说这种索引“可能有点让人难以理解”,因此我们俩在此都遇到麻烦也就不足为奇了：-).