# xorbits.numpy.diag#

xorbits.numpy.diag(v, k=0, sparse=None, gpu=None, chunk_size=None)[source]#

Extract a diagonal or construct a diagonal array.

See the more detailed documentation for `numpy.diagonal` if you use this function to extract a diagonal and wish to write to the resulting array; whether it returns a copy or a view depends on what version of numpy you are using.

Parameters
• v (array_like) – If v is a 2-D array, return a copy of its k-th diagonal. If v is a 1-D array, return a 2-D array with v on the k-th diagonal.

• k (int, optional) – Diagonal in question. The default is 0. Use k>0 for diagonals above the main diagonal, and k<0 for diagonals below the main diagonal.

Returns

out – The extracted diagonal or constructed diagonal array.

Return type

ndarray

`diagonal`

Return specified diagonals.

`diagflat`

Create a 2-D array with the flattened input as a diagonal.

`trace`

Sum along diagonals.

`triu`

Upper triangle of an array.

`tril`

Lower triangle of an array.

Examples

```>>> x = np.arange(9).reshape((3,3))
>>> x
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
```
```>>> np.diag(x)
array([0, 4, 8])
>>> np.diag(x, k=1)
array([1, 5])
>>> np.diag(x, k=-1)
array([3, 7])
```
```>>> np.diag(np.diag(x))
array([[0, 0, 0],
[0, 4, 0],
[0, 0, 8]])
```
sparse: bool, optional

Create sparse tensor if True, False as default

gpubool, optional

Allocate the tensor on GPU if True, False as default

chunk_sizeint or tuple of int or tuple of ints, optional

Desired chunk size on each dimension

This docstring was copied from numpy.