Attributes

dtype : dtype

Data type of the array

shape : 2-tuple

Shape of the array

ndim : int

Number of dimensions (this is always 2)

nnz :

Number of stored values, including explicit zeros

data :

DIA format data array of the array

offsets :

DIA format offset array of the array

This can be instantiated in several ways:

dia_array(D)

with a dense array

dia_array(S)

with another sparse array 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)

Notes

Sparse arrays can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and array power.

Sparse array with DIAgonal storage

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.]])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.sparse._arrays.dia_array

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