This is documentation for an old release of SciPy (version 0.10.1). Read this page in the documentation of the latest stable release (version 1.15.1).
Sparse matrix with DIAgonal storage
Notes
Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power.
Examples
>>> from scipy.sparse import *
>>> from scipy import *
>>> dia_matrix( (3,4), dtype=int8).todense()
matrix([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]], dtype=int8)
>>> data = array([[1,2,3,4]]).repeat(3,axis=0)
>>> offsets = array([0,-1,2])
>>> dia_matrix( (data,offsets), shape=(4,4)).todense()
matrix([[1, 0, 3, 0],
[1, 2, 0, 4],
[0, 2, 3, 0],
[0, 0, 3, 4]])
Attributes
dtype | |
shape | |
ndim | int(x[, base]) -> integer |
nnz | number of nonzero values |
data | DIA format data array of the matrix |
offsets | DIA format offset array of the matrix |
Methods
asformat(format) | Return this matrix in a given sparse format |
asfptype() | Upcast matrix to a floating point format (if necessary) |
astype(t) | |
conj() | |
conjugate() | |
copy() | |
diagonal() | Returns the main diagonal of the matrix |
dot(other) | |
getH() | |
get_shape() | |
getcol(j) | Returns a copy of column j of the matrix, as an (m x 1) sparse |
getformat() | |
getmaxprint() | |
getnnz() | number of nonzero values |
getrow(i) | Returns a copy of row i of the matrix, as a (1 x n) sparse |
mean([axis]) | Average the matrix over the given axis. |
multiply(other) | Point-wise multiplication by another matrix |
nonzero() | nonzero indices |
reshape(shape) | |
set_shape(shape) | |
setdiag(values[, k]) | Fills the diagonal elements {a_ii} with the values from the given sequence. |
sum([axis]) | Sum the matrix over the given axis. |
toarray() | |
tobsr([blocksize]) | |
tocoo() | |
tocsc() | |
tocsr() | |
todense() | |
todia([copy]) | |
todok() | |
tolil() | |
transpose() |