bmat#
- scipy.sparse.bmat(blocks, format=None, dtype=None)[source]#
- Build a sparse array or matrix from sparse sub-blocks - Note: - block_arrayis preferred over- bmat. They are the same function except that- bmatreturns a deprecated sparse matrix when none of the inputs are sparse arrays.- Warning - This function returns a sparse matrix when no inputs are sparse arrays. You are encouraged to use - block_arrayto 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 - 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]])