scipy.sparse.bsr_matrix

class scipy.sparse.bsr_matrix(arg1, shape=None, dtype=None, copy=False, blocksize=None)

Block Sparse Row matrix

This can be instantiated in several ways:

bsr_matrix(D, [blocksize=(R,C)])

with a dense matrix or rank-2 ndarray D

bsr_matrix(S, [blocksize=(R,C)])

with another sparse matrix S (equivalent to S.tobsr())

bsr_matrix((M, N), [blocksize=(R,C), dtype])

to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype=’d’.

bsr_matrix((data, ij), [blocksize=(R,C), shape=(M, N)])

where data and ij satisfy a[ij[0, k], ij[1, k]] = data[k]

bsr_matrix((data, indices, indptr), [shape=(M, N)])

is the standard BSR representation where the block column indices for row i are stored in indices[indptr[i]:indices[i+1]] and their corresponding block values are stored in data[ indptr[i]: indptr[i+1] ]. If the shape parameter is not supplied, the matrix dimensions are inferred from the index arrays.

Notes

Summary

• The Block Compressed Row (BSR) format is very similar to the Compressed Sparse Row (CSR) format. BSR is appropriate for sparse matrices with dense sub matrices like the last example below. Block matrices often arise in vector-valued finite element discretizations. In such cases, BSR is considerably more efficient than CSR and CSC for many sparse arithmetic operations.

Blocksize

• The blocksize (R,C) must evenly divide the shape of the matrix (M,N). That is, R and C must satisfy the relationship M % R = 0 and N % C = 0.
• If no blocksize is specified, a simple heuristic is applied to determine an appropriate blocksize.

Examples

>>> from scipy.sparse import *
>>> from scipy import *
>>> bsr_matrix( (3,4), dtype=int8 ).todense()
matrix([[0, 0, 0, 0],
        [0, 0, 0, 0],
        [0, 0, 0, 0]], dtype=int8)

>>> row  = array([0,0,1,2,2,2])
>>> col  = array([0,2,2,0,1,2])
>>> data = array([1,2,3,4,5,6])
>>> bsr_matrix( (data,(row,col)), shape=(3,3) ).todense()
matrix([[1, 0, 2],
        [0, 0, 3],
        [4, 5, 6]])

>>> indptr  = array([0,2,3,6])
>>> indices = array([0,2,2,0,1,2])
>>> data    = array([1,2,3,4,5,6]).repeat(4).reshape(6,2,2)
>>> bsr_matrix( (data,indices,indptr), shape=(6,6) ).todense()
matrix([[1, 1, 0, 0, 2, 2],
        [1, 1, 0, 0, 2, 2],
        [0, 0, 0, 0, 3, 3],
        [0, 0, 0, 0, 3, 3],
        [4, 4, 5, 5, 6, 6],
        [4, 4, 5, 5, 6, 6]])

Methods

asformat(format)	Return this matrix in a given sparse format
asfptype()	Upcast matrix to a floating point format (if necessary)
astype(t)
check_format([full_check])	check whether the matrix format is valid
conj()
conjugate()
copy()
diagonal()	Returns the main diagonal of the matrix
dot(other)
eliminate_zeros()
getH()
get_shape()
getcol(j)	Returns a copy of column j of the matrix, as an (m x 1) sparse
getdata(ind)
getformat()
getmaxprint()
getnnz()
getrow(i)	Returns a copy of row i of the matrix, as a (1 x n) sparse
matmat(other)
matvec(other)
mean([axis])	Average the matrix over the given axis.
multiply(other)	Point-wise multiplication by another matrix
nonzero()	nonzero indices
prune()	Remove empty space after all non-zero elements.
reshape(shape)
set_shape(shape)
setdiag(values[, k])	Fills the diagonal elements {a_ii} with the values from the given sequence.
sort_indices()	Sort the indices of this matrix in place
sorted_indices()	Return a copy of this matrix with sorted indices
sum([axis])	Sum the matrix over the given axis.
sum_duplicates()
toarray()
tobsr([blocksize, copy])
tocoo([copy])	Convert this matrix to COOrdinate format.
tocsc()
tocsr()
todense()
todia()
todok()
tolil()
transpose()