Putting together a larger matrix from smaller matrices

[sturlamolden]

On 25 Aug, 17:37, Matjaz Bezovnik <mbezov... at freenet.si> wrote:

> Scott, thank you very much for the snippet.
>
> It is exactly what I looked for; simple to read and obvious as to what
> it does even a month later to a non-pythonist!


Since you were talking about matrices, observe that numpy has a matrix
subclass of ndarray, which e.g. changes the meaning of the * operator
mean indicate matrix multiplication. Thus,

>>> import numpy as np
>>> np.eye(4)
array([[ 1.,  0.,  0.,  0.],
       [ 0.,  1.,  0.,  0.],
       [ 0.,  0.,  1.,  0.],
       [ 0.,  0.,  0.,  1.]])
>>> np.matrix(np.eye(4))
matrix([[ 1.,  0.,  0.,  0.],
        [ 0.,  1.,  0.,  0.],
        [ 0.,  0.,  1.,  0.],
        [ 0.,  0.,  0.,  1.]])

>>> a = np.ones((4,4))
>>> a*a
array([[ 1.,  1.,  1.,  1.],
       [ 1.,  1.,  1.,  1.],
       [ 1.,  1.,  1.,  1.],
       [ 1.,  1.,  1.,  1.]])
>>> b = np.matrix(a)
>>> b*b
matrix([[ 4.,  4.,  4.,  4.],
        [ 4.,  4.,  4.,  4.],
        [ 4.,  4.,  4.,  4.],
        [ 4.,  4.,  4.,  4.]])
>>> b**2
matrix([[ 4.,  4.,  4.,  4.],
        [ 4.,  4.,  4.,  4.],
        [ 4.,  4.,  4.,  4.],
        [ 4.,  4.,  4.,  4.]])
>>> b**4
matrix([[ 64.,  64.,  64.,  64.],
        [ 64.,  64.,  64.,  64.],
        [ 64.,  64.,  64.,  64.],
        [ 64.,  64.,  64.,  64.]])

In Matlab, you use .* vs. * and .^ vs. ^ to obtain the same effect. In
NumPy we use different classes for arrays and matrices.



Sturla Molden