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# 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` |
  `asfptype` |
  `astype` |
  `check_format` |
  `conj` |
  `conjugate` |
  `copy` | Generic (shallow and deep) copying operations.
  `diagonal` |
  `dot` |
  `eliminate_zeros` |
  `getH` |
  `get_shape` |
  `getcol` |
  `getdata` |
  `getformat` |
  `getmaxprint` |
  `getnnz` |
  `getrow` |
  `matmat` |
  `matvec` |
  `mean` |
  `multiply` |
  `nonzero` |
  `prune` |
  `reshape` |
  `set_shape` |
  `setdiag` |
  `sort_indices` |
  `sorted_indices` |
  `sum` |
  `sum_duplicates` |
  `toarray` |
  `tobsr` |
  `tocoo` |
  `tocsc` |
  `tocsr` |
  `todense` |
  `todia` |
  `todok` |
  `tolil` |
  `transpose` |

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