matrix_sparse_usage.pycmd

// matrix_sparse_usage.pycmd
// 
// Copyright (c) 2014 - Michele De Stefano (micdestefano@users.sourceforge.net)
// 
// Distributed under the MIT License (See accompanying file LICENSE)


/**
 * \example    matrix_sparse_usage.pycmd
 * 
 * Shows the usage of the matrix_sparse_usage extension from the Python prompt.
 */

mds_utils::python::ublas::matrix_sparse.hpp functionalities usage example from the Python prompt:

>>> import matrix_sparse_usage as mu
>>> from scipy.sparse import csr_matrix,csc_matrix
>>> import numpy as np
>>> M = csr_matrix((4, 3))
>>> M[0,1] = 1.1; M[3,0] = 2.2; M[2,2] = 3.3
/usr/lib64/python2.7/site-packages/scipy/sparse/compressed.py:496: SparseEfficiencyWarning: changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.
  SparseEfficiencyWarning)
>>> M.todense()
matrix([[ 0. ,  1.1,  0. ],
        [ 0. ,  0. ,  0. ],
        [ 0. ,  0. ,  3.3],
        [ 2.2,  0. ,  0. ]])
>>> mu.get_matrix(M)
Matrix got:
[4,3]((0,1.1,0),(0,0,0),(0,0,3.3),(2.2,0,0))
>>> mu.get_matrix_int(M)
Matrix got:
[4,3]((0,1,0),(0,0,0),(0,0,3),(2,0,0))
>>> M = csr_matrix((4, 3),dtype=np.complex128)
>>> M[0,1] = 1.1*(1.+1.j); M[3,0] = 2.2*(1.+1.j); M[2,2] = 3.3*(1.+1.j)
>>> M.todense()
matrix([[ 0.0+0.j ,  1.1+1.1j,  0.0+0.j ],
        [ 0.0+0.j ,  0.0+0.j ,  0.0+0.j ],
        [ 0.0+0.j ,  0.0+0.j ,  3.3+3.3j],
        [ 2.2+2.2j,  0.0+0.j ,  0.0+0.j ]])
>>> mu.get_matrix_cplx(M)
Matrix got:
[4,3](((0,0),(1.1,1.1),(0,0)),((0,0),(0,0),(0,0)),((0,0),(0,0),(3.3,3.3)),((2.2,2.2),(0,0),(0,0)))
>>> mu.get_matrix_fcplx(M)
Matrix got:
[4,3](((0,0),(1.1,1.1),(0,0)),((0,0),(0,0),(0,0)),((0,0),(0,0),(3.3,3.3)),((2.2,2.2),(0,0),(0,0)))
>>> A = csc_matrix((4, 3))
>>> A[0,1] = 1.1; A[3,0] = 2.2; A[2,2] = 3.3
/usr/lib64/python2.7/site-packages/scipy/sparse/compressed.py:496: SparseEfficiencyWarning: changing the sparsity structure of a csc_matrix is expensive. lil_matrix is more efficient.
  SparseEfficiencyWarning)
>>> A.todense()
matrix([[ 0. ,  1.1,  0. ],
        [ 0. ,  0. ,  0. ],
        [ 0. ,  0. ,  3.3],
        [ 2.2,  0. ,  0. ]])
>>> mu.get_csc_matrix(A)
Matrix got:
[4,3]((0,1.1,0),(0,0,0),(0,0,3.3),(2.2,0,0))
>>> mu.get_matrix(A)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "matrix_sparse_usage.py", line 81, in get_matrix
    return _matrix_sparse_usage.get_matrix(*args)
RuntimeError: Cannot get a uBLAS compressed matrix from the given parameter.
Check the data type and the storage layout (i.e. csr or csc).
>>> M = mu.create_ublas_compressed_matrix()
>>> M
<Swig Object of type 'boost::numeric::ublas::compressed_matrix< double > *' at 0x2878330>
>>> M.__class__
<type 'SwigPyObject'>
>>> mu.get_matrix_no_conversion(M)
Matrix got:
[4,3]((0,1.1,0),(0,0,0),(0,0,3.3),(2.2,0,0))
>>> mu.destroy_ublas_compressed_matrix(M)
>>> M
<Swig Object of type 'boost::numeric::ublas::compressed_matrix< double > *' at 0x2260b10>
>>> B = mu.create_csr_matrix()
>>> B.__class__
<class 'scipy.sparse.csr.csr_matrix'>
>>> B
<4x3 sparse matrix of type '<type 'numpy.float64'>'
   with 3 stored elements in Compressed Sparse Row format>
>>> B.todense()
matrix([[ 0. ,  1.1,  0. ],
        [ 0. ,  0. ,  0. ],
        [ 0. ,  0. ,  3.3],
        [ 2.2,  0. ,  0. ]])
>>> C = mu.create_csc_matrix_fcplx()
>>> C.__class__
<class 'scipy.sparse.csc.csc_matrix'>
>>> C
<4x3 sparse matrix of type '<type 'numpy.complex64'>'
   with 3 stored elements in Compressed Sparse Column format>
>>> C.todense()
matrix([[ 0.00000000+0.j        ,  1.10000002+1.10000002j,  0.00000000+0.j        ],
        [ 0.00000000+0.j        ,  0.00000000+0.j        ,  0.00000000+0.j        ],
        [ 0.00000000+0.j        ,  0.00000000+0.j        ,
          3.29999995+3.29999995j],
        [ 2.20000005+2.20000005j,  0.00000000+0.j        ,  0.00000000+0.j        ]], dtype=complex64)