flopscope.

flopscope.numpy.triu_indices

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

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

Adapted from NumPy docs np.triu_indices

Areacore
Typefree
NumPy Refnp.triu_indices
Cost
0
Flopscope Context

Return upper-triangle indices for n x n array.

Parameters

n:int

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

k:int, optional

Diagonal offset (see triu 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, shape(2) of ndarrays, shape(`n`)

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 upper triangular part starting at the main diagonal, and one starting two diagonals further right:

>>> iu1 = flops.triu_indices(4)
>>> iu1
(array([0, 0, 0, 0, 1, 1, 1, 2, 2, 3]), array([0, 1, 2, 3, 1, 2, 3, 2, 3, 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[iu1]
array([ 0,  1,  2, ..., 10, 11, 15])

And for assigning values:

>>> a[iu1] = -1
>>> a
array([[-1, -1, -1, -1],
       [ 4, -1, -1, -1],
       [ 8,  9, -1, -1],
       [12, 13, 14, -1]])

These cover only a small part of the whole array (two diagonals right of the main one):

>>> iu2 = flops.triu_indices(4, 2)
>>> a[iu2] = -10
>>> a
array([[ -1,  -1, -10, -10],
       [  4,  -1,  -1, -10],
       [  8,   9,  -1,  -1],
       [ 12,  13,  14,  -1]])