Attributes

dtype : dtype

Data type of the array

shape : 2-tuple

Shape of the array

ndim : int

Number of dimensions (this is always 2)

nnz :

Number of stored values, including explicit zeros

data :

Data array of the array

indices :

BSR format index array

indptr :

BSR format index pointer array

blocksize :

Block size of the array

has_sorted_indices :

Whether indices are sorted

This can be instantiated in several ways:

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

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

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

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

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

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

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

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

bsr_array((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 array dimensions are inferred from the index arrays.

Notes

Sparse arrays can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and array 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 arrays with dense sub arrays like the last example below. Block arrays 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 array (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 array

Examples

>>> from scipy.sparse import bsr_array
... bsr_array((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_array((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_array((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.

scipy.sparse._arrays.bsr_array

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