python numpy 矩阵左右翻转/上下翻转

Posted 2022-03-09 xiaoniu-666

numpy API:

• flattened
• flip() (in module numpy)
• fliplr() (in module numpy)
• flipud() (in module numpy)

 

flip:

flip(m, 0) is equivalent to flipud(m).

flip(m, 1) is equivalent to fliplr(m).

flip(m, n) corresponds to m[...,::-1,...] with ::-1 at position n.

flip(m) corresponds to m[::-1,::-1,...,::-1] with ::-1 at all positions.

flip(m, (0, 1)) corresponds to m[::-1,::-1,...] with ::-1 at position 0 and position 1.

>>> A = np.arange(8).reshape((2,2,2))
>>> A
array([[[0, 1],
        [2, 3]],
       [[4, 5],
        [6, 7]]])
>>> flip(A, 0)
array([[[4, 5],
        [6, 7]],
       [[0, 1],
        [2, 3]]])
>>> flip(A, 1)
array([[[2, 3],
        [0, 1]],
       [[6, 7],
        [4, 5]]])
>>> np.flip(A)
array([[[7, 6],
        [5, 4]],
       [[3, 2],
        [1, 0]]])
>>> np.flip(A, (0, 2))
array([[[5, 4],
        [7, 6]],
       [[1, 0],
        [3, 2]]])
>>> A = np.random.randn(3,4,5)
>>> np.all(flip(A,2) == A[:,:,::-1,...])
True

flipud: (==flip(m, 1) )

Flip array in the up/down direction.

Flip the entries in each column in the up/down direction. Rows are preserved, but appear in a different order than before.

Equivalent to m[::-1,...]. Does not require the array to be two-dimensional.

>>> A = np.diag([1.0, 2, 3])
>>> A
array([[ 1.,  0.,  0.],
       [ 0.,  2.,  0.],
       [ 0.,  0.,  3.]])
>>> np.flipud(A)
array([[ 0.,  0.,  3.],
       [ 0.,  2.,  0.],
       [ 1.,  0.,  0.]])
>>>
>>> A = np.random.randn(2,3,5)
>>> np.all(np.flipud(A) == A[::-1,...])
True
>>>
>>> np.flipud([1,2])
array([2, 1])

 

fliplr: (==flip(m, 0))

　　Equivalent to m[:,::-1]. Requires the array to be at least 2-D.

        Flip array in the left/right direction.
    rot90
        Rotate array counterclockwise.

>>> A = np.diag([1.,2.,3.])
>>> A
array([[ 1.,  0.,  0.],
       [ 0.,  2.,  0.],
       [ 0.,  0.,  3.]])
>>> np.fliplr(A)
array([[ 0.,  0.,  1.],
       [ 0.,  2.,  0.],
       [ 3.,  0.,  0.]])
>>>
>>> A = np.random.randn(2,3,5)
>>> np.all(np.fliplr(A) == A[:,::-1,...])
True