xorbits.numpy.diagflat#

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

Create a two-dimensional array with the flattened input as a diagonal.

Parameters

  • v (array_like) – Input data, which is flattened and set as the k-th diagonal of the output.

  • k (int, optional) – Diagonal to set; 0, the default, corresponds to the “main” diagonal, a positive (negative) k giving the number of the diagonal above (below) the main.

Returns

out – The 2-D output array.

Return type

ndarray

See also

diag

MATLAB work-alike for 1-D and 2-D arrays.

diagonal

Return specified diagonals.

trace

Sum along diagonals.

Examples

>>> np.diagflat([[1,2], [3,4]])  
array([[1, 0, 0, 0],
       [0, 2, 0, 0],
       [0, 0, 3, 0],
       [0, 0, 0, 4]])

>>> np.diagflat([1,2], 1)  
array([[0, 1, 0],
       [0, 0, 2],
       [0, 0, 0]])
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.