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dia_matrix — SciPy v1.17.0.dev Manual

scipy.sparse.

class scipy.sparse.dia_matrix(arg1, shape=None, dtype=None, copy=False, *, maxprint=None)[source]#

Sparse matrix with DIAgonal storage.

This can be instantiated in several ways:

dia_matrix(D)

where D is a 2-D ndarray

dia_matrix(S)

with another sparse array or matrix S (equivalent to S.todia())

dia_matrix((M, N), [dtype])

to construct an empty matrix with shape (M, N), dtype is optional, defaulting to dtype=’d’.

dia_matrix((data, offsets), shape=(M, N))

where the data[k,:] stores the diagonal entries for diagonal offsets[k] (See example below)

Attributes:

dtypedtype

Data type of the matrix

shape2-tuple

Shape of the matrix

ndimint

Number of dimensions (this is always 2)

nnz

Number of stored values, including explicit zeros.

size

Number of stored values.

data

DIA format data array of the matrix

offsets

DIA format offset array of the matrix

T

Transpose.

Methods

Notes

Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power. Sparse matrices with DIAgonal storage do not support slicing.

Examples

>>> import numpy as np
>>> from scipy.sparse import dia_matrix
>>> dia_matrix((3, 4), dtype=np.int8).toarray()
array([[0, 0, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0]], dtype=int8)

>>> data = np.array([[1, 2, 3, 4]]).repeat(3, axis=0)
>>> offsets = np.array([0, -1, 2])
>>> dia_matrix((data, offsets), shape=(4, 4)).toarray()
array([[1, 0, 3, 0],
       [1, 2, 0, 4],
       [0, 2, 3, 0],
       [0, 0, 3, 4]])

>>> from scipy.sparse import dia_matrix
>>> n = 10
>>> ex = np.ones(n)
>>> data = np.array([ex, 2 * ex, ex])
>>> offsets = np.array([-1, 0, 1])
>>> dia_matrix((data, offsets), shape=(n, n)).toarray()
array([[2., 1., 0., ..., 0., 0., 0.],
       [1., 2., 1., ..., 0., 0., 0.],
       [0., 1., 2., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 2., 1., 0.],
       [0., 0., 0., ..., 1., 2., 1.],
       [0., 0., 0., ..., 0., 1., 2.]])

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