scipy.sparse.diags¶
- scipy.sparse.diags(diagonals, offsets, shape=None, format=None, dtype=None)[source]¶
Construct a sparse matrix from diagonals.
New in version 0.11.
Parameters: diagonals : sequence of array_like
Sequence of arrays containing the matrix diagonals, corresponding to offsets.
offsets : sequence of int
- Diagonals to set:
- k = 0 the main diagonal
- k > 0 the k-th upper diagonal
- k < 0 the k-th lower diagonal
shape : tuple of int, optional
Shape of the result. If omitted, a square matrix large enough to contain the diagonals is returned.
format : {“dia”, “csr”, “csc”, “lil”, ...}, optional
Matrix format of the result. By default (format=None) an appropriate sparse matrix format is returned. This choice is subject to change.
dtype : dtype, optional
Data type of the matrix.
See also
- spdiags
- construct matrix from diagonals
Notes
This function differs from spdiags in the way it handles off-diagonals.
The result from diags is the sparse equivalent of:
np.diag(diagonals[0], offsets[0]) + ... + np.diag(diagonals[k], offsets[k])
Repeated diagonal offsets are disallowed.
Examples
>>> diagonals = [[1,2,3,4], [1,2,3], [1,2]] >>> diags(diagonals, [0, -1, 2]).todense() matrix([[1, 0, 1, 0], [1, 2, 0, 2], [0, 2, 3, 0], [0, 0, 3, 4]])
Broadcasting of scalars is supported (but shape needs to be specified):
>>> diags([1, -2, 1], [-1, 0, 1], shape=(4, 4)).todense() matrix([[-2., 1., 0., 0.], [ 1., -2., 1., 0.], [ 0., 1., -2., 1.], [ 0., 0., 1., -2.]])
If only one diagonal is wanted (as in numpy.diag), the following works as well:
>>> diags([1, 2, 3], 1).todense() matrix([[ 0., 1., 0., 0.], [ 0., 0., 2., 0.], [ 0., 0., 0., 3.], [ 0., 0., 0., 0.]])