scipy.sparse.diags#
- scipy.sparse.diags(diagonals, offsets=0, shape=None, format=None, dtype=None)[source]#
Construct a sparse matrix from diagonals.
Warning
This function returns a sparse matrix – not a sparse array. You are encouraged to use
diags_array
to take advantage of the sparse array functionality.- Parameters:
- diagonalssequence of array_like
Sequence of arrays containing the matrix diagonals, corresponding to offsets.
- offsetssequence of int or an int, optional
- Diagonals to set:
k = 0 the main diagonal (default)
k > 0 the kth upper diagonal
k < 0 the kth lower diagonal
- shapetuple 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.
- dtypedtype, optional
Data type of the matrix.
See also
spdiags
construct matrix from diagonals
diags_array
construct sparse array instead of sparse matrix
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.
New in version 0.11.
Examples
>>> from scipy.sparse import diags >>> diagonals = [[1, 2, 3, 4], [1, 2, 3], [1, 2]] >>> diags(diagonals, [0, -1, 2]).toarray() array([[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)).toarray() array([[-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).toarray() array([[ 0., 1., 0., 0.], [ 0., 0., 2., 0.], [ 0., 0., 0., 3.], [ 0., 0., 0., 0.]])