dpnp.tril_indices

dpnp.tril_indices(n, k=0, m=None, device=None, usm_type='device', sycl_queue=None)[source]

Return the indices for the lower-triangle of an (n, m) array.

For full documentation refer to numpy.tril_indices.

Parameters:

  • n (int) -- The row dimension of the arrays for which the returned indices will be valid.

  • k (int, optional) -- Diagonal offset (see dpnp.tril for details). Default: 0.

  • m ({None, int}, optional) -- The column dimension of the arrays for which the returned arrays will be valid. By default m is taken equal to n. Default: None.

  • 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:

inds -- The indices for the triangle. The returned tuple contains two arrays, each with the indices along one dimension of the array.

Return type:

tuple of dpnp.ndarray

See also

dpnp.triu_indices

similar function, for upper-triangular.

dpnp.mask_indices

generic function accepting an arbitrary mask function.

dpnp.tril

Return lower triangle of an array.

dpnp.triu

Return upper triangle of an array.

Examples

Compute two different sets of indices to access 4x4 arrays, one for the lower triangular part starting at the main diagonal, and one starting two diagonals further right:

>>> import dpnp as np
>>> il1 = np.tril_indices(4)
>>> il2 = np.tril_indices(4, 2)

Here is how they can be used with a sample array:

>>> 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]])

Both for indexing:

>>> a[il1]
array([ 0,  4,  5, ..., 13, 14, 15])

And for assigning values:

>>> a[il1] = -1
>>> a
array([[-1,  1,  2,  3],
       [-1, -1,  6,  7],
       [-1, -1, -1, 11],
       [-1, -1, -1, -1]])

These cover almost the whole array (two diagonals right of the main one):

>>> a[il2] = -10
>>> a
array([[-10, -10, -10,   3],
       [-10, -10, -10, -10],
       [-10, -10, -10, -10],
       [-10, -10, -10, -10]])