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

See also

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.