scipy.linalg.block_diag#
- scipy.linalg.block_diag(*arrs)[source]#
Create a block diagonal matrix from provided arrays.
Given the inputs A, B and C, the output will have these arrays arranged on the diagonal:
[[A, 0, 0], [0, B, 0], [0, 0, C]]
- Parameters:
- A, B, C, …array_like, up to 2-D
Input arrays. A 1-D array or array_like sequence of length n is treated as a 2-D array with shape
(1,n)
.
- Returns:
- Dndarray
Array with A, B, C, … on the diagonal. D has the same dtype as A.
Notes
If all the input arrays are square, the output is known as a block diagonal matrix.
Empty sequences (i.e., array-likes of zero size) will not be ignored. Noteworthy, both [] and [[]] are treated as matrices with shape
(1,0)
.Examples
>>> import numpy as np >>> from scipy.linalg import block_diag >>> A = [[1, 0], ... [0, 1]] >>> B = [[3, 4, 5], ... [6, 7, 8]] >>> C = [[7]] >>> P = np.zeros((2, 0), dtype='int32') >>> block_diag(A, B, C) array([[1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0], [0, 0, 3, 4, 5, 0], [0, 0, 6, 7, 8, 0], [0, 0, 0, 0, 0, 7]]) >>> block_diag(A, P, B, C) array([[1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 3, 4, 5, 0], [0, 0, 6, 7, 8, 0], [0, 0, 0, 0, 0, 7]]) >>> block_diag(1.0, [2, 3], [[4, 5], [6, 7]]) array([[ 1., 0., 0., 0., 0.], [ 0., 2., 3., 0., 0.], [ 0., 0., 0., 4., 5.], [ 0., 0., 0., 6., 7.]])