Question

I got some sparse matrix like this

>>>import numpy as np
>>>from scipy.sparse import *
>>>A = csr_matrix((np.identity(3)))

>>>print A
  (0, 0)    1.0
  (1, 1)    1.0
  (2, 2)    1.0

For better understanding A is something like this:

>>>print A.todense()
[[ 1.  0.  0.]
 [ 0.  1.  0.]
 [ 0.  0.  1.]]

And I would like to have an operator (let us call it op1(n) ) doing this:

>>>A.op1(1)
[[ 0.  1.  0.]
 [ 0.  0.  1.]
 [ 1.  0.  0.]]

=> makes the last n columns the first n ones,
so

>>>A == A.op1(3)  
true

. Is there some build-in solution, (EDIT:) that returns a sparse matrix again?
The solution with roll:

X = np.roll(X.todense(),-tau, axis = 0)
print X.__class__

returns

<class 'numpy.matrixlib.defmatrix.matrix'>

Answer 1

scipy.sparse doesn’t have roll, but you can simulate it with hstack:

from scipy.sparse import *
A = eye(3, 3, format='csr')
hstack((A[:, 1:], A[:, :1]), format='csr')    # roll left
hstack((A[:, -1:], A[:, :-1]), format='csr')  # roll right