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Let A, x, y and z be some vectors or matrices of appropriate size. Then in MATLAB one can build a "super matrix" B out of them very easily:
A = [1 2;3 4];
x = [4;5];
y = [1 2];
z = 4;
B = [A x;y z];
The output is:
>> B
B =
1 2 4
3 4 5
1 2 4
What is the best way to achieve the same effect in NumPy?
823
Concatenating Matrices You can also use square brackets to append existing matrices. This way of creating a matrix is called concatenation. For example, concatenate two row vectors to make an even longer row vector. To arrange A and B as two rows of a matrix, use the semicolon.
You can use the square bracket operator [] to concatenate or append arrays. For example, [A,B] and [A B] concatenates arrays A and B horizontally, and [A; B] concatenates them vertically.
Or you can use cell. For ex: a = [1,2;3,4]; b = ones(3,3); c = {a,b}; Like that you can put different matrix size in a same variable...
NumPy arrays are the equivalent to the basic array data structure in MATLAB. With NumPy arrays, you can do things like inner and outer products, transposition, and element-wise operations.
You can use numpy.block:
In [27]: a
Out[27]:
array([[1, 2],
[3, 4]])
In [28]: x
Out[28]:
array([[4],
[5]])
In [29]: y
Out[29]: array([1, 2])
In [30]: z
Out[30]: 4
In [31]: np.block([[a, x], [y, z]])
Out[31]:
array([[1, 2, 4],
[3, 4, 5],
[1, 2, 4]])
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