dpnp.tri¶

dpnp.tri(N, /, M=None, k=0, dtype=<class 'float'>, *, device=None, usm_type='device', sycl_queue=None)[source]¶

An array with ones at and below the given diagonal and zeros elsewhere.

For full documentation refer to numpy.tri.

Parameters:

Nint

Number of rows in the array.

M{None, int}, optional

Number of columns in the array. By default, M is taken equal to N.

Default: None.

kint, optional

The sub-diagonal at and below which the array is filled. k = 0 is the main diagonal, while k < 0 is below it, and k > 0 is above.

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: float.

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 of shape (N, M): Array with its lower triangle filled with ones and zeros elsewhere.

See also

dpnp.tril: Return lower triangle of an array.
dpnp.triu: Return upper triangle of an array.

Examples

>>> import dpnp as np
>>> np.tri(3, 5, 2, dtype=int)
array([[1, 1, 1, 0, 0],
       [1, 1, 1, 1, 0],
       [1, 1, 1, 1, 1]])

>>> np.tri(3, 5, -1)
array([[0.,  0.,  0.,  0.,  0.],
       [1.,  0.,  0.,  0.,  0.],
       [1.,  1.,  0.,  0.,  0.]])

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

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

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

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