dpnp.diagonal

dpnp.diagonal(a, offset=0, axis1=0, axis2=1)[source]

Return specified diagonals.

For full documentation refer to numpy.diagonal.

Parameters:

a ({dpnp.ndarray, usm_ndarray}) -- Array from which the diagonals are taken.
offset (int, optional) -- Offset of the diagonal from the main diagonal. Can be positive or negative. Defaults to main diagonal (0).
axis1 (int, optional) -- Axis to be used as the first axis of the 2-D sub-arrays from which the diagonals should be taken. Defaults to first axis (0).
axis2 (int, optional) -- Axis to be used as the second axis of the 2-D sub-arrays from which the diagonals should be taken. Defaults to second axis (1).

Returns:

array_of_diagonals -- If a is 2-D, then a 1-D array containing the diagonal and of the same type as a is returned. If a.ndim > 2, then the dimensions specified by axis1 and axis2 are removed, and a new axis inserted at the end corresponding to the diagonal.

Return type:

dpnp.ndarray

See also

dpnp.diag: Extract a diagonal or construct a diagonal array.
dpnp.diagflat: Create a two-dimensional array with the flattened input as a diagonal.
dpnp.trace: Return the sum along diagonals of the array.

Examples

>>> import dpnp as np
>>> a = np.arange(4).reshape(2,2)
>>> a
array([[0, 1],
       [2, 3]])
>>> a.diagonal()
array([0, 3])
>>> a.diagonal(1)
array([1])

A 3-D example:

>>> a = np.arange(8).reshape(2,2,2)
>>> a
array([[[0, 1],
        [2, 3]],
       [[4, 5],
        [6, 7]]])
>>> a.diagonal(0,  # Main diagonals of two arrays created by skipping
...            0,  # across the outer(left)-most axis last and
...            1)  # the "middle" (row) axis first.
array([[0, 6],
       [1, 7]])

The sub-arrays whose main diagonals we just obtained; note that each corresponds to fixing the right-most (column) axis, and that the diagonals are "packed" in rows.

>>> a[:,:,0]  # main diagonal is [0 6]
array([[0, 2],
       [4, 6]])
>>> a[:,:,1]  # main diagonal is [1 7]
array([[1, 3],
       [5, 7]])

The anti-diagonal can be obtained by reversing the order of elements using either dpnp.flipud or dpnp.fliplr.

>>> a = np.arange(9).reshape(3, 3)
>>> a
array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])
>>> np.fliplr(a).diagonal()  # Horizontal flip
array([2, 4, 6])
>>> np.flipud(a).diagonal()  # Vertical flip
array([6, 4, 2])

Note that the order in which the diagonal is retrieved varies depending on the flip function.