Attributes

dtype : dtype

Data type of the matrix

shape : 2-tuple

Shape of the matrix

ndim : int

Number of dimensions (this is always 2)

nnz :

Number of stored values, including explicit zeros

data :

Data array of the matrix

indices :

BSR format index array

indptr :

BSR format index pointer array

blocksize :

Block size of the matrix

has_sorted_indices :

Whether indices are sorted

This can be instantiated in several ways:

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

where D is a dense matrix or 2-D ndarray.

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]:indptr[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

Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power.

Summary of BSR format

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.

Block Sparse Row matrix

Examples

>>> from scipy.sparse import bsr_matrix
... bsr_matrix((3, 4), dtype=np.int8).toarray()
array([[0, 0, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0]], dtype=int8)

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

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

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.dtypes.common.is_sparse scipy.sparse._bsr.bsr_matrix pandas.core.dtypes.common.is_extension_type pandas.core.dtypes.common.is_scipy_sparse scipy.sparse._bsr.isspmatrix_bsr

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