dpnp.eye¶

dpnp.eye(N, /, M=None, k=0, dtype=None, order='C', *, like=None, device=None, usm_type='device', sycl_queue=None)[source]¶

Return a 2-D array with ones on the diagonal and zeros elsewhere.

For full documentation refer to numpy.eye.

Parameters:

Nint

Number of rows in the output.

M{None, int}, optional

Number of columns in the output. If None, defaults to N.

Default: None.

kint, optional

Index of the diagonal: 0 (the default) refers to the main diagonal, a positive value refers to an upper diagonal, and a negative value to a lower diagonal.

Default: 0.

dtype{None, str, dtype object}, optional

The desired dtype for the array, e.g., dpnp.int32. If None, uses a default floating point data type for the device on which the input array is allocated.

Default: None.

order{None, "C", "F"}, optional

Memory layout of the newly output array.

Default: "C".

device{None, string, SyclDevice, SyclQueue, Device}, optional

An array API concept of device where the output array is created. device can be None, a oneAPI filter selector string, an instance of dpctl.SyclDevice corresponding to a non-partitioned SYCL device, an instance of dpctl.SyclQueue, or a dpnp.tensor.Device object returned by dpnp.ndarray.device.

Default: None.

usm_type{None, "device", "shared", "host"}, optional

The type of SYCL USM allocation for the output array.

Default: "device".

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:

outdpnp.ndarray: An array where all elements are equal to zero, except for the k-th diagonal, whose values are equal to one.

Limitations

Parameter like is supported only with default value None. Otherwise, the function raises NotImplementedError exception.

See also

dpnp.identity: Return the identity array.
dpnp.diag: Extract a diagonal or construct a diagonal array.

Examples

>>> import dpnp as np
>>> np.eye(2, dtype=int)
array([[1, 0],
       [0, 1]])

>>> np.eye(3, k=1)
array([[0.,  1.,  0.],
       [0.,  0.,  1.],
       [0.,  0.,  0.]])

Creating an array on a different device or with a specified usm_type

>>> x = np.eye(2, dtype=int) # default case
>>> x, x.device, x.usm_type
(array([[1, 0],
        [0, 1]]), Device(level_zero:gpu:0), 'device')

>>> y = np.eye(2, dtype=int, device="cpu")
>>> y, y.device, y.usm_type
(array([[1, 0],
        [0, 1]]), Device(opencl:cpu:0), 'device')

>>> z = np.eye(2, dtype=int, usm_type="host")
>>> z, z.device, z.usm_type
(array([[1, 0],
        [0, 1]]), Device(level_zero:gpu:0), 'host')