flopscope.

flopscope.numpy.tril_indices

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

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

Adapted from NumPy docs np.tril_indices

Areacore
Typefree
NumPy Refnp.tril_indices
Cost
0
Flopscope Context

Return lower-triangle indices for n x n array.

Parameters

n:int

The row dimension of the arrays for which the returned indices will be valid.

k:int, optional

Diagonal offset (see tril for details).

m:int, optional

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

Returns

inds:tuple of arrays

The row and column indices, respectively. The row indices are sorted in non-decreasing order, and the correspdonding column indices are strictly increasing for each row.

See also

Examples

>>> import flopscope.numpy as fnp

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 = flops.tril_indices(4)
>>> il1
(array([0, 1, 1, 2, 2, 2, 3, 3, 3, 3]), array([0, 0, 1, 0, 1, 2, 0, 1, 2, 3]))

Note that row indices (first array) are non-decreasing, and the corresponding column indices (second array) are strictly increasing for each row. Here is how they can be used with a sample array:

>>> a = flops.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):

>>> il2 = flops.tril_indices(4, 2)
>>> a[il2] = -10
>>> a
array([[-10, -10, -10,   3],
       [-10, -10, -10, -10],
       [-10, -10, -10, -10],
       [-10, -10, -10, -10]])