# xorbits.numpy.triu_indices_from#

xorbits.numpy.triu_indices_from(arr, k=0)#

Return the indices for the upper-triangle of arr.

See triu_indices for full details.

Parameters
• arr (ndarray, shape(N, N)) – The indices will be valid for square arrays.

• k (int, optional) – Diagonal offset (see triu for details).

Returns

triu_indices_from – Indices for the upper-triangle of arr.

Return type

tuple, shape(2) of ndarray, shape(N)

Examples

Create a 4 by 4 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]])
```

Pass the array to get the indices of the upper triangular elements.

```>>> triui = np.triu_indices_from(a)
>>> triui
(array([0, 0, 0, 0, 1, 1, 1, 2, 2, 3]), array([0, 1, 2, 3, 1, 2, 3, 2, 3, 3]))
```
```>>> a[triui]
array([ 0,  1,  2,  3,  5,  6,  7, 10, 11, 15])
```

This is syntactic sugar for triu_indices().

```>>> np.triu_indices(a.shape[0])
(array([0, 0, 0, 0, 1, 1, 1, 2, 2, 3]), array([0, 1, 2, 3, 1, 2, 3, 2, 3, 3]))
```

Use the k parameter to return the indices for the upper triangular array from the k-th diagonal.

```>>> triuim1 = np.triu_indices_from(a, k=1)
>>> a[triuim1]
array([ 1,  2,  3,  6,  7, 11])
```

Notes

New in version 1.4.0(numpy).

Warning

This method has not been implemented yet. Xorbits will try to execute it with numpy.

This docstring was copied from numpy.