Am hoping someone can explain to me the following behavior I observe with a numpy array:
>>> 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!
I don't understand this transposing behavior and it is very inconvenient for what I want to do. Could anyone explain it to me? An alternative method for getting that subset of data_block would be a great bonus.
You can achieve your desired result this way:
>>> data_block[0,:,:][:,ind_slice].shape
(480L, 300L)
I confess I don't have a complete understanding of how complicated numpy indexing works, but the documentation seems to hint at the trouble you're having:
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 likex[ind1][...,ind2,:]under basic slicing.Warning: The above is not true for advanced slicing.
and. . .
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).
Thus you are triggering that behavior by indexing with your ind_slice array instead of a regular slice.
The documentation itself says that this kind of indexing "can be somewhat mind-boggling to understand", so it's not surprising we both have trouble with this :-).