import numpy as np
A = np.array([[1, 2],
[3, 4]])
B = np.array([[5, 6],
[7, 8]])
C = np.array([[1, 2, 0, 0],
[3, 4, 0, 0],
[0, 0, 5, 6],
[0, 0, 7, 8]])
I would like to make C directly from A and B, are there any simply ways to construct a diagonal array C? Thanks.
Approach #1 : One easy way would be with np.bmat -
Z = np.zeros((2,2),dtype=int) # Create off-diagonal zeros array
out = np.asarray(np.bmat([[A, Z], [Z, B]]))
Sample run -
In [24]: Z = np.zeros((2,2),dtype=int)
In [25]: np.asarray(np.bmat([[A, Z], [Z, B]]))
Out[25]:
array([[1, 2, 0, 0],
[3, 4, 0, 0],
[0, 0, 5, 6],
[0, 0, 7, 8]])
Approach #2 : For generic number of arrays, we can use masking -
def diag_block_mat_boolindex(L):
shp = L[0].shape
mask = np.kron(np.eye(len(L)), np.ones(shp))==1
out = np.zeros(np.asarray(shp)*len(L),dtype=int)
out[mask] = np.concatenate(L).ravel()
return out
Approach #3 : For generic number of arrays, another way with multi-dimensional slicing -
def diag_block_mat_slicing(L):
shp = L[0].shape
N = len(L)
r = range(N)
out = np.zeros((N,shp[0],N,shp[1]),dtype=int)
out[r,:,r,:] = L
return out.reshape(np.asarray(shp)*N)
Sample runs -
In [137]: A = np.array([[1, 2],
...: [3, 4]])
...: B = np.array([[5, 6],
...: [7, 8]])
...: C = np.array([[11, 12],
...: [13, 14]])
...: D = np.array([[15, 16],
...: [17, 18]])
...:
In [138]: diag_block_mat_boolindex((A,B,C,D))
Out[138]:
array([[ 1, 2, 0, 0, 0, 0, 0, 0],
[ 3, 4, 0, 0, 0, 0, 0, 0],
[ 0, 0, 5, 6, 0, 0, 0, 0],
[ 0, 0, 7, 8, 0, 0, 0, 0],
[ 0, 0, 0, 0, 11, 12, 0, 0],
[ 0, 0, 0, 0, 13, 14, 0, 0],
[ 0, 0, 0, 0, 0, 0, 15, 16],
[ 0, 0, 0, 0, 0, 0, 17, 18]])
In [139]: diag_block_mat_slicing((A,B,C,D))
Out[139]:
array([[ 1, 2, 0, 0, 0, 0, 0, 0],
[ 3, 4, 0, 0, 0, 0, 0, 0],
[ 0, 0, 5, 6, 0, 0, 0, 0],
[ 0, 0, 7, 8, 0, 0, 0, 0],
[ 0, 0, 0, 0, 11, 12, 0, 0],
[ 0, 0, 0, 0, 13, 14, 0, 0],
[ 0, 0, 0, 0, 0, 0, 15, 16],
[ 0, 0, 0, 0, 0, 0, 17, 18]])
np.array? It looks like it is np.matrix now.
Here's a recursive solution that does does not require that the output array is square. The input is a list of 2-D arrays.
import numpy as np
def diag_mat(rem=[], result=np.empty((0, 0))):
if not rem:
return result
m = rem.pop(0)
result = np.block(
[
[result, np.zeros((result.shape[0], m.shape[1]))],
[np.zeros((m.shape[0], result.shape[1])), m],
]
)
return diag_mat(rem, result)
Testing the output:
>>> a = np.array([[2, 1, 5], [7, 3, 1]])
>>> b = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> diag_mat([a, b])
array([[2., 1., 5., 0., 0., 0.],
[7., 3., 1., 0., 0., 0.],
[0., 0., 0., 1., 2., 3.],
[0., 0., 0., 4., 5., 6.],
[0., 0., 0., 7., 8., 9.]])
Cyour desired output or what?