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bundles / scipy 1.17.1 / scipy / sparse / _construct / diags

function

scipy.sparse._construct:diags

source: /scipy/sparse/_construct.py :445

Signature

def   diags ( diagonals ,  offsets = 0 ,  shape = None ,  format = None ,  dtype = <object object at 0x0000> )

Summary

Construct a sparse matrix from diagonals.

Extended Summary

Parameters

diagonals : sequence of array_like

Sequence of arrays containing the matrix diagonals, corresponding to offsets.

offsets : sequence 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

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. 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_matrix : dia_matrix

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

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.

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()
✓
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()
✓
If only one diagonal is wanted (as in `numpy.diag`), the following works as well: 
diags([1.0, 2.0, 3.0], 1).toarray()
✗

See also

diags_array

construct sparse array instead of sparse matrix

spdiags

construct matrix from diagonals

Aliases

  • scipy.sparse.diags

Referenced by

This package