dpnp.diag_indices

dpnp.diag_indices(n, ndim=2, device=None, usm_type='device', sycl_queue=None)[source]

Return the indices to access the main diagonal of an array.

This returns a tuple of indices that can be used to access the main diagonal of an array a with a.ndim >= 2 dimensions and shape (n, n, ..., n). For a.ndim = 2 this is the usual diagonal, for a.ndim > 2 this is the set of indices to access a[i, i, ..., i] for i = [0..n-1].

For full documentation refer to numpy.diag_indices.

Parameters:

  • n (int) -- The size, along each dimension, of the arrays for which the returned indices can be used.

  • ndim (int, optional) -- The number of dimensions. Default: 2.

  • device ({None, string, SyclDevice, SyclQueue}, optional) -- An array API concept of device where the output array is created. The device can be None (the default), an OneAPI filter selector string, an instance of dpctl.SyclDevice corresponding to a non-partitioned SYCL device, an instance of dpctl.SyclQueue, or a Device object returned by dpnp.dpnp_array.dpnp_array.device property.

  • usm_type ({"device", "shared", "host"}, optional) -- The type of SYCL USM allocation for the output array.

  • sycl_queue ({None, SyclQueue}, optional) -- A SYCL queue to use for output array allocation and copying. The sycl_queue can be passed as None (the default), which means to get the SYCL queue from device keyword if present or to use a default queue. Default: None.

Returns:

out -- The indices to access the main diagonal of an array.

Return type:

tuple of dpnp.ndarray

See also

dpnp.diag_indices_from

Return the indices to access the main diagonal of an n-dimensional array.

Examples

Create a set of indices to access the diagonal of a (4, 4) array:

>>> import dpnp as np
>>> di = np.diag_indices(4)
>>> di
(array([0, 1, 2, 3]), array([0, 1, 2, 3]))
>>> a = np.arange(16).reshape(4, 4)
>>> a
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11],
       [12, 13, 14, 15]])
>>> a[di] = 100
>>> a
array([[100,   1,   2,   3],
       [  4, 100,   6,   7],
       [  8,   9, 100,  11],
       [ 12,  13,  14, 100]])

Now, we create indices to manipulate a 3-D array:

>>> d3 = np.diag_indices(2, 3)
>>> d3
(array([0, 1]), array([0, 1]), array([0, 1]))

And use it to set the diagonal of an array of zeros to 1:

>>> a = np.zeros((2, 2, 2), dtype=int)
>>> a[d3] = 1
>>> a
array([[[1, 0],
        [0, 0]],
       [[0, 0],
        [0, 1]]])