Best way to create block matrices from separate blocks in numpy?
Consider the code
M=5;N=3;
A11=np.random.rand(M,M);
A12=np.random.rand(M,N);
A21=np.random.rand(N,M);
A22=np.random.rand(N,N);
I am new to numpy and am learning it. I want to create a matrix of blocks as follows
RowBlock1=np.concatenate((A11,A12),axis=1)
RowBlock2=np.concatenate((A21,A22),axis=1)
Block=np.concatenate((RowBlock1,RowBlock2),axis=0)
Is there an easier way to do this? For example, in matlab, I would do
Block=[[A11,A12];[A21,A22]]
and will be done with it. I understand this is only reserved for arrays.
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Starting with 1.13 NumPy, numpy.block
:
Block = numpy.block([[A11, A12], [A21, A22]])
For previous versions bmat
:
Block = numpy.bmat([[A11, A12], [A21, A22]])
numpy.bmat
creates a matrix, not an array. This is usually bad. You can call asarray
the result if you want an array, or use an attribute A
:
Block = numpy.bmat([[A11, A12], [A21, A22]]).A
bmat
also does some stack frame operations for you to do this:
Block = numpy.bmat('A11,A12; A21,A22')
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