Data type of the matrix
Shape of the matrix
Number of dimensions (this is always 2)
Number of stored values, including explicit zeros
DIA format data array of the matrix
DIA format offset array of the matrix
This can be instantiated in several ways:
dia_matrix(D)
with a dense matrix
dia_matrix(S)
with another sparse 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)
Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power.
Sparse matrix with DIAgonal storage
>>> 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_matrixSee :
... 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.]])
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.sparse.linalg._eigen.lobpcg.lobpcg.lobpcg
scipy.sparse._dia.isspmatrix_dia
scipy.sparse._dia.dia_matrix
scipy.sparse._construct.spdiags
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