bmat — SciPy v1.15.2 Manual (original) (raw)

scipy.sparse.

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().

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]])