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dia_array — SciPy v1.16.1 Manual

scipy.sparse.

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

Sparse array with DIAgonal storage.

This can be instantiated in several ways:

dia_array(D)

where D is a 2-D ndarray

dia_array(S)

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

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

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

dia_array((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 array

shape2-tuple

Shape of the array

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 array

offsets

DIA format offset array of the array

T

Transpose.

Methods

Notes

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

Examples

>>> import numpy as np
>>> from scipy.sparse import dia_array
>>> dia_array((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_array((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_array
>>> n = 10
>>> ex = np.ones(n)
>>> data = np.array([ex, 2 * ex, ex])
>>> offsets = np.array([-1, 0, 1])
>>> dia_array((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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