scipy.sparse.

diags#

scipy.sparse.diags(diagonals, offsets=0, shape=None, format=None, dtype=<object object>)[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 (repeated offsets are not allowed):

  • 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. If dtype is None, the output data type is determined by the data type of the input diagonals.

Up until SciPy 1.19, the default behavior will be to return a matrix with an inexact (floating point) data type. In particular, integer input will be converted to double precision floating point. This behavior is deprecated, and in SciPy 1.19, the default behavior will be changed to return a matrix with the same data type as the input diagonals. To adopt this behavior before version 1.19, use dtype=None.

Returns:
new_matrixdia_matrix

dia_matrix holding the values in diagonals offset from the main diagonal as indicated in offsets.

See also

spdiags

construct matrix from diagonals

diags_array

construct sparse array instead of sparse matrix

Notes

Repeated diagonal offsets are disallowed.

The result from diags is the sparse equivalent of:

np.diag(diagonals[0], offsets[0])
+ ...
+ np.diag(diagonals[k], offsets[k])

diags differs from dia_matrix in the way it handles off-diagonals. Specifically, dia_matrix assumes the data input includes padding (ignored values) at the start/end of the rows for positive/negative offset, while diags assumes the input data has no padding. Each value in the input diagonals is used.

Added in version 0.11.

Examples

>>> from scipy.sparse import diags
>>> diagonals = [[1.0, 2.0, 3.0, 4.0], [1.0, 2.0, 3.0], [1.0, 2.0]]
>>> 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([1.0, -2.0, 1.0], [-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([1.0, 2.0, 3.0], 1).toarray()
array([[ 0.,  1.,  0.,  0.],
       [ 0.,  0.,  2.,  0.],
       [ 0.,  0.,  0.,  3.],
       [ 0.,  0.,  0.,  0.]])
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