I thought I understood the reshape function in Numpy until I was messing around with it and came across this example:
a = np.arange(16).reshape((4,4))
which returns:
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]])
This makes sense to me, but then when I do:
a.reshape((2,8), order = 'F')
it returns:
array([[0, 8, 1, 9, 2, 10, 3, 11],
[4, 12, 5, 13, 6, 14, 7, 15]])
I would expect it to return:
array([[0, 4, 8, 12, 1, 5, 9, 13],
[2, 6, 10, 14, 3, 7, 11, 15]])
Can someone please explain what is happening here?
The elements of a in order 'F'
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]])
are [0,4,8,12,1,5,9 ...]
Now rearrange them in a (2,8) array.
I think the reshape docs talks about raveling the elements, and then reshaping them. Evidently the ravel is done first.
Experiment with a.ravel(order='F').reshape(2,8).
Oops, I get what you expected:
In [208]: a = np.arange(16).reshape(4,4)
In [209]: a
Out[209]:
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]])
In [210]: a.ravel(order='F')
Out[210]: array([ 0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15])
In [211]: _.reshape(2,8)
Out[211]:
array([[ 0, 4, 8, 12, 1, 5, 9, 13],
[ 2, 6, 10, 14, 3, 7, 11, 15]])
OK, I have to keep the 'F' order during the reshape
In [214]: a.ravel(order='F').reshape(2,8, order='F')
Out[214]:
array([[ 0, 8, 1, 9, 2, 10, 3, 11],
[ 4, 12, 5, 13, 6, 14, 7, 15]])
In [215]: a.ravel(order='F').reshape(2,8).flags
Out[215]:
C_CONTIGUOUS : True
F_CONTIGUOUS : False
...
In [216]: a.ravel(order='F').reshape(2,8, order='F').flags
Out[216]:
C_CONTIGUOUS : False
F_CONTIGUOUS : True
From np.reshape docs
You can think of reshaping as first raveling the array (using the given
index order), then inserting the elements from the raveled array into the
new array using the same kind of index ordering as was used for the
raveling.
The notes on order are fairly long, so it's not surprising that the topic is confusing.
