scipy.sparse.bmat#

scipy.sparse.bmat(blocks, format=None, dtype=None)[source]#

Build a sparse array or matrix from sparse sub-blocks

Note: block_array is preferred over bmat. They are the same function except that bmat can return a deprecated sparse matrix. bmat returns a coo_matrix if none of the inputs are a sparse array.

Warning

This function returns a sparse matrix – not a sparse array. You are encouraged to use block_array to take advantage of the sparse array functionality.

Parameters:

blocksarray_like: Grid of sparse matrices with compatible shapes. An entry of None implies an all-zero matrix.
format{‘bsr’, ‘coo’, ‘csc’, ‘csr’, ‘dia’, ‘dok’, ‘lil’}, optional: The sparse format of the result (e.g. “csr”). By default an appropriate sparse matrix format is returned. This choice is subject to change.
dtypedtype, optional: The data-type of the output matrix. If not given, the dtype is determined from that of blocks.

Returns:

bmatsparse matrix or array

If any block in blocks is a sparse array, return a sparse array. Otherwise return a sparse matrix.

If you want a sparse array built from blocks that are not sparse arrays, use block_array().

See also

block_array

Examples

>>> from scipy.sparse import coo_array, bmat
>>> A = coo_array([[1, 2], [3, 4]])
>>> B = coo_array([[5], [6]])
>>> C = coo_array([[7]])
>>> bmat([[A, B], [None, C]]).toarray()
array([[1, 2, 5],
       [3, 4, 6],
       [0, 0, 7]])

>>> bmat([[A, None], [None, C]]).toarray()
array([[1, 2, 0],
       [3, 4, 0],
       [0, 0, 7]])