New in version 1.0.1.

Higher-Level Sparse Matrix Classes

The pysparseMatrix module

class pysparseMatrix.PysparseMatrix(**kwargs)

Bases: sparseMatrix.SparseMatrix

A PysparseMatrix is a class wrapper for the pysparse spmatrix sparse matrix type. This class facilitates matrix populating and allows intuitive operations on sparse matrices and vectors.

Currently accepted keywords include:  

nrow	The number of rows of the matrix
ncol	The number of columns of the matrix
size	The common number of rows and columns, for a square matrix
bandwidth	The bandwidth (if creating a band matrix)
matrix	The starting spmatrix if there is one
sizeHint	A guess on the number of nonzero elements of the matrix
symmetric	A boolean indicating whether the matrix is symmetric.

addAt(vector, id1, id2)

Add elements of vector to the positions in the matrix corresponding to (id1,id2)

>>> L = PysparseMatrix(size = 3)
>>> L.put([3.,10.,numpy.pi,2.5], [0,0,1,2], [2,1,1,0])
>>> L.addAt((1.73,2.2,8.4,3.9,1.23), (1,2,0,0,1), (2,2,0,0,2))
>>> print L
12.300000  10.000000   3.000000  
    ---     3.141593   2.960000  
 2.500000      ---     2.200000  

addAtDiagonal(vector)

Add the components of vector vector to the diagonal elements of the matrix.

copy()

Returns a (deep) copy of a sparse matrix

exportMmf(filename)

Exports the matrix to a Matrix Market file of the given filename.

find()

Returns three Numpy arrays to describe the sparsity pattern of self in so-called coordinate (or triplet) format:

>>> L = PysparseMatrix(size = 3)
>>> L.put([3.,10.,numpy.pi,2.5], [0,0,1,2], [2,1,1,0])
>>> (val,irow,jcol) = L.find()
>>> val
array([ 10.        ,   3.        ,   3.14159265,   2.5       ])
>>> irow
array([0, 0, 1, 2])
>>> jcol
array([1, 2, 1, 0])

getMatrix()

Returns the underlying ll_mat sparse matrix of self

getNnz()

Returns the number of nonzero elements of self

getNumpyArray()

Convert a sparse matrix to a dense Numpy matrix.

getShape()

Returns the shape (nrow,ncol) of a sparse matrix

isSymmetric()

Returns True is self is a symmetric matrix or False otherwise

matvec(x)

This method is required for scipy solvers.

put(value, id1, id2)

Put elements of value at positions of the matrix corresponding to (id1, id2)

>>> L = PysparseMatrix(size = 3)
>>> L.put( [3.,10.,numpy.pi,2.5], [0,0,1,2], [2,1,1,0] )
>>> print L
    ---    10.000000   3.000000  
    ---     3.141593      ---    
 2.500000      ---        ---    
>>> L.put(2*numpy.pi, range(3), range(3))
>>> print L
 6.283185  10.000000   3.000000  
    ---     6.283185      ---    
 2.500000      ---     6.283185  

If value is a scalar, it has the same effect as the vector of appropriate length with all values equal to value. If id1 is omitted, it is replaced with range(nrow). If id2 is omitted, it is replaced with range(ncol).

putDiagonal(vector)

Put elements of vector along diagonal of matrix

>>> L = PysparseMatrix(size = 3)
>>> L.putDiagonal([3.,10.,numpy.pi])
>>> print L
 3.000000      ---        ---    
    ---    10.000000      ---    
    ---        ---     3.141593  
>>> L.putDiagonal([10.,3.])
>>> print L
10.000000      ---        ---    
    ---     3.000000      ---    
    ---        ---     3.141593  
>>> L.putDiagonal(2.7182)
>>> print L
 2.718200      ---        ---    
    ---     2.718200      ---    
    ---        ---     2.718200  

take(id1, id2)

Extract elements at positions (irow[i], jcol[i]) and place them in the array val. In other words:

for i in range(len(val)): val[i] = A[irow[i],jcol[i]]

takeDiagonal()

Extract the diagonal of a matrix and place it in a Numpy array.

Creating an Identity Matrix

class pysparseMatrix.PysparseIdentityMatrix(size)

Bases: pysparseMatrix.PysparseMatrix

Represents a sparse identity matrix for pysparse.

>>> print PysparseIdentityMatrix(size = 3)
 1.000000      ---        ---    
    ---     1.000000      ---    
    ---        ---     1.000000  

Creating Sparse Matrices from Diagonals

class pysparseMatrix.PysparseSpDiagsMatrix(size, vals, pos, **kwargs)

Bases: pysparseMatrix.PysparseMatrix

Represents a banded matrix with specified diagonals.

Example: Create a tridiagonal matrix with 1’s on the diagonal, 2’s above the diagonal, and -2’s below the diagonal.

>>> from numpy import ones
>>> e = ones(5)
>>> print PysparseSpDiagsMatrix(size=5, vals=(-2*e,e,2*e), pos=(-1,0,1))
 1.000000   2.000000      ---        ---        ---    
-2.000000   1.000000   2.000000      ---        ---    
    ---    -2.000000   1.000000   2.000000      ---    
    ---        ---    -2.000000   1.000000   2.000000  
    ---        ---        ---    -2.000000   1.000000  

Note that since the pos[k]-th diagonal has size-|pos[k]| elements, only that many first elements of vals[k] will be inserted.

If the banded matrix is requested to be symmetric, elements above the main diagonal are not inserted.

Fancy Indexing

Fancy indexing carries over to PysparseMatrix objects and is used exactly in the same way as with ll_mat objects. Refer to Section Low-Level Sparse Matrix Types for details.