numpy.tril_indices#

numpy.tril_indices(n, k=0, m=None)[source]#

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

Parameters:

nint: The row dimension of the arrays for which the returned indices will be valid.
kint, optional: Diagonal offset (see tril for details).
mint, optional: New in version 1.9.0.

The column dimension of the arrays for which the returned arrays will be valid. By default m is taken equal to n.

Returns:

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

See also

triu_indices: similar function, for upper-triangular.
mask_indices: generic function accepting an arbitrary mask function.
tril, triu

Notes

New in version 1.4.0.

Examples

>>> import numpy as np

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:

>>> 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]])