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scipy.sparse.

# bsr_array

class scipy.sparse.bsr_array(arg1, shape=None, dtype=None, copy=False, blocksize=None, *, maxprint=None)

  Block Sparse Row format sparse array.

  This can be instantiated in several ways:
    bsr_array(D, [blocksize=(R,C)])

      where D is a 2-D ndarray.

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

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

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

      to construct an empty sparse 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.

  Attributes:
    data ndarray

      BSR format data array of the array

    indices ndarray

      BSR format index array of the array

    indptr ndarray

      BSR format index pointer array of the array

    `blocksize` 2-tuple of integers

      Block size of the matrix.

    `has_sorted_indices` bool

      Whether the indices are sorted

    `has_canonical_format` bool

      Whether the array/matrix has sorted indices and no duplicates

    dtype dtype

      Data type of the array

    shape 2-tuple

      Shape of the array

    ndim int

      Number of dimensions (this is always 2)

    `format` str

      Format string for matrix.

    `nnz` int

      Number of stored values, including explicit zeros.

    `size` int

      Number of stored values.

    `T` bsr_array

      Transpose.

    `mT` bsr_array

      Matrix transpose.

  Methods

  `__len__`() |
  `arcsin`() | Element-wise arcsin.
  `arcsinh`() | Element-wise arcsinh.
  `arctan`() | Element-wise arctan.
  `arctanh`() | Element-wise arctanh.
  `argmax`([axis, out, explicit]) | Return indices of maximum elements along an axis.
  `argmin`([axis, out, explicit]) | Return indices of minimum elements along an axis.
  `asformat`(format[, copy]) | Return this array/matrix in the passed format.
  `astype`(dtype[, casting, copy]) | Cast the array/matrix elements to a specified type.
  `ceil`() | Element-wise ceil.
  `check_format`([full_check]) | Check whether the array/matrix respects the BSR format.
  `conj`([copy]) | Element-wise complex conjugation.
  `conjugate`([copy]) | Element-wise complex conjugation.
  `copy`() | Returns a copy of this array/matrix.
  `count_nonzero`([axis]) | Number of non-zero entries.
  `deg2rad`() | Element-wise deg2rad.
  `diagonal`([k]) | Returns the kth diagonal of the array/matrix.
  `dot`(other) | Ordinary dot product.
  `eliminate_zeros`() | Remove zero elements in-place.
  `expm1`() | Element-wise expm1.
  `floor`() | Element-wise floor.
  `log1p`() | Element-wise log1p.
  `max`([axis, out, explicit]) | Return the maximum of the array/matrix or maximum along an axis.
  `maximum`(other) | Element-wise maximum between this and another array/matrix.
  `mean`([axis, dtype, out]) | Compute the arithmetic mean along the specified axis.
  `min`([axis, out, explicit]) | Return the minimum of the array/matrix or maximum along an axis.
  `minimum`(other) | Element-wise minimum between this and another array/matrix.
  `multiply`(other) | Element-wise multiplication by another array/matrix.
  `nanmax`([axis, out, explicit]) | Return the maximum, ignoring any Nans, along an axis.
  `nanmin`([axis, out, explicit]) | Return the minimum, ignoring any Nans, along an axis.
  `nonzero`() | Nonzero indices of the array/matrix.
  `power`(n[, dtype]) | This function performs element-wise power.
  `prune`() | Remove empty space after all non-zero elements.
  `rad2deg`() | Element-wise rad2deg.
  `reshape`(self, shape[, order, copy]) | Gives a new shape to a sparse array/matrix without changing its data.
  `resize`(*shape) | Resize the array/matrix in-place to dimensions given by `shape`.
  `rint`() | Element-wise rint.
  `setdiag`(values[, k]) | Set diagonal or off-diagonal elements of the array/matrix.
  `sign`() | Element-wise sign.
  `sin`() | Element-wise sin.
  `sinh`() | Element-wise sinh.
  `sort_indices`() | Sort the indices of this array/matrix in place.
  `sorted_indices`() | Return a copy of this array/matrix with sorted indices.
  `sqrt`() | Element-wise sqrt.
  `sum`([axis, dtype, out]) | Sum the array/matrix elements over a given axis.
  `sum_duplicates`() | Eliminate duplicate array/matrix entries by adding them together.
  `tan`() | Element-wise tan.
  `tanh`() | Element-wise tanh.
  `toarray`([order, out]) | Return a dense ndarray representation of this sparse array/matrix.
  `tobsr`([blocksize, copy]) | Convert this array/matrix to Block Sparse Row format.
  `tocoo`([copy]) | Convert this array/matrix to COOrdinate format.
  `tocsc`([copy]) | Convert this array/matrix to Compressed Sparse Column format.
  `tocsr`([copy]) | Convert this array/matrix to Compressed Sparse Row format.
  `todense`([order, out]) | Return a dense representation of this sparse array.
  `todia`([copy]) | Convert this array/matrix to sparse DIAgonal format.
  `todok`([copy]) | Convert this array/matrix to Dictionary Of Keys format.
  `tolil`([copy]) | Convert this array/matrix to List of Lists format.
  `trace`([offset]) | Returns the sum along diagonals of the sparse array/matrix.
  `transpose`([axes, copy]) | Reverses the dimensions of the sparse array/matrix.
  `trunc`() | Element-wise trunc.

  __add__ |
  __getitem__ |
  __matmul__ |
  __mul__ |
  __pow__ |
  __rmatmul__ |
  __rmul__ |
  __truediv__ |

  Notes

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

  Summary of BSR format

  The Block Sparse 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. Such sparse 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 sparse 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.

  Canonical Format

  In canonical format, there are no duplicate blocks and indices are sorted per row.

  Limitations

  Block Sparse Row format sparse arrays do not support slicing.

  Examples

      >>> import numpy as np
      >>> 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]])