`scipy.sparse._dia:dia_matrix`

source: /scipy/sparse/_dia.py :580

Signature

   def     dia_matrix (    arg1    ,    shape    =  None   ,    dtype    =  None   ,    copy    =  False   ,  * ,     maxprint    =  None     )

Summary

Sparse matrix with DIAgonal storage.

Extended Summary

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

dtype : dtype: Data type of the matrix
shape : 2-tuple: Shape of the matrix
ndim : int: Number of dimensions (this is always 2)
nnz
size
data: DIA format data array of the matrix
offsets: DIA format offset array of the matrix
T

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()

✓

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()

✓

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()

✓

Aliases

scipy.sparse.dia_matrix