dpnp.diagflat¶

dpnp.diagflat(v, /, k=0, *, device=None, usm_type=None, sycl_queue=None)[source]¶

Create a two-dimensional array with the flattened input as a diagonal.

For full documentation refer to numpy.diagflat.

Parameters:

varray_like

Input data, which is flattened and set as the k-th diagonal of the output, in any form that can be converted to an array. This includes scalars, lists, lists of tuples, tuples, tuples of tuples, tuples of lists, and ndarrays.

kint, optional

Diagonal to set; 0, the default, corresponds to the "main" diagonal, a positive (negative) k giving the number of the diagonal above (below) the main.

Default: 0.

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: None.

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: The 2-D output array.

See also

dpnp.diag: Extract a diagonal or construct a diagonal array.
dpnp.diagonal: Return specified diagonals.
dpnp.trace: Return sum along diagonals.

Examples

>>> import dpnp as np
>>> x0 = np.array([[1, 2], [3, 4]])
>>> np.diagflat(x0)
array([[1, 0, 0, 0],
       [0, 2, 0, 0],
       [0, 0, 3, 0],
       [0, 0, 0, 4]])

>>> np.diagflat(x0, 1)
array([[0, 1, 0, 0, 0],
       [0, 0, 2, 0, 0],
       [0, 0, 0, 3, 0],
       [0, 0, 0, 0, 4],
       [0, 0, 0, 0, 0]])

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

>>> x = np.diagflat(x0) # default case
>>> x, x.device, x.usm_type
array([[1, 0, 0, 0],
       [0, 2, 0, 0],
       [0, 0, 3, 0],
       [0, 0, 0, 4]]), Device(level_zero:gpu:0), 'device')

>>> y = np.diagflat(x0, device="cpu")
>>> y, y.device, y.usm_type
array([[1, 0, 0, 0],
       [0, 2, 0, 0],
       [0, 0, 3, 0],
       [0, 0, 0, 4]]), Device(opencl:cpu:0), 'device')

>>> z = np.diagflat(x0, usm_type="host")
>>> z, z.device, z.usm_type
array([[1, 0, 0, 0],
       [0, 2, 0, 0],
       [0, 0, 3, 0],
       [0, 0, 0, 4]]), Device(level_zero:gpu:0), 'host')