# scipy.sparse.dia_matrix¶

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

Sparse matrix with DIAgonal storage

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)

Notes

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

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]])


Attributes

 shape Get shape of a matrix. nnz Number of stored values, including explicit zeros.
 dtype (dtype) Data type of the matrix ndim (int) Number of dimensions (this is always 2) data DIA format data array of the matrix offsets DIA format offset array of the matrix

Methods

 arcsin() Element-wise arcsin. arcsinh() Element-wise arcsinh. arctan() Element-wise arctan. arctanh() Element-wise arctanh. asformat(format) Return this matrix in a given sparse format asfptype() Upcast matrix to a floating point format (if necessary) astype(t) Cast the matrix elements to a specified type. ceil() Element-wise ceil. conj() Element-wise complex conjugation. conjugate() Element-wise complex conjugation. copy() Returns a copy of this matrix. count_nonzero() Number of non-zero entries, equivalent to deg2rad() Element-wise deg2rad. diagonal() Returns the main diagonal of the matrix dot(other) Ordinary dot product expm1() Element-wise expm1. floor() Element-wise floor. getH() Return the Hermitian transpose of this matrix. get_shape() Get shape of a matrix. getcol(j) Returns a copy of column j of the matrix, as an (m x 1) sparse matrix (column vector). getformat() Format of a matrix representation as a string. getmaxprint() Maximum number of elements to display when printed. getnnz([axis]) Number of stored values, including explicit zeros. getrow(i) Returns a copy of row i of the matrix, as a (1 x n) sparse matrix (row vector). log1p() Element-wise log1p. maximum(other) Element-wise maximum between this and another matrix. mean([axis, dtype, out]) Compute the arithmetic mean along the specified axis. minimum(other) Element-wise minimum between this and another matrix. multiply(other) Point-wise multiplication by another matrix nonzero() nonzero indices power(n[, dtype]) This function performs element-wise power. rad2deg() Element-wise rad2deg. reshape(shape[, order]) Gives a new shape to a sparse matrix without changing its data. rint() Element-wise rint. set_shape(shape) See reshape. setdiag(values[, k]) Set diagonal or off-diagonal elements of the array. sign() Element-wise sign. sin() Element-wise sin. sinh() Element-wise sinh. sqrt() Element-wise sqrt. sum([axis, dtype, out]) Sum the matrix elements over a given axis. tan() Element-wise tan. tanh() Element-wise tanh. toarray([order, out]) Return a dense ndarray representation of this matrix. tobsr([blocksize, copy]) Convert this matrix to Block Sparse Row format. tocoo([copy]) Convert this matrix to COOrdinate format. tocsc([copy]) Convert this matrix to Compressed Sparse Column format. tocsr([copy]) Convert this matrix to Compressed Sparse Row format. todense([order, out]) Return a dense matrix representation of this matrix. todia([copy]) Convert this matrix to sparse DIAgonal format. todok([copy]) Convert this matrix to Dictionary Of Keys format. tolil([copy]) Convert this matrix to LInked List format. transpose([axes, copy]) Reverses the dimensions of the sparse matrix. trunc() Element-wise trunc.

#### Previous topic

scipy.sparse.csr_matrix.trunc

#### Next topic

scipy.sparse.dia_matrix.shape