sparse.extract

Functions to extract parts of sparse matrices

Module Contents

Functions

find(A) Return the indices and values of the nonzero elements of a matrix
tril(A,k=0,format=None) Return the lower triangular portion of a matrix in sparse format
triu(A,k=0,format=None) Return the upper triangular portion of a matrix in sparse format
_masked_coo(A,mask)
find(A)

Return the indices and values of the nonzero elements of a matrix

A : dense or sparse matrix
Matrix whose nonzero elements are desired.
(I,J,V) : tuple of arrays
I,J, and V contain the row indices, column indices, and values of the nonzero matrix entries.
>>> from scipy.sparse import csr_matrix, find
>>> A = csr_matrix([[7.0, 8.0, 0],[0, 0, 9.0]])
>>> find(A)
(array([0, 0, 1], dtype=int32), array([0, 1, 2], dtype=int32), array([ 7.,  8.,  9.]))
tril(A, k=0, format=None)

Return the lower triangular portion of a matrix in sparse format

Returns the elements on or below the k-th diagonal of the matrix A.
  • k = 0 corresponds to the main diagonal
  • k > 0 is above the main diagonal
  • k < 0 is below the main diagonal
A : dense or sparse matrix
Matrix whose lower trianglar portion is desired.
k : integer : optional
The top-most diagonal of the lower triangle.
format : string
Sparse format of the result, e.g. format=”csr”, etc.
L : sparse matrix
Lower triangular portion of A in sparse format.

triu : upper triangle in sparse format

>>> from scipy.sparse import csr_matrix, tril
>>> A = csr_matrix([[1, 2, 0, 0, 3], [4, 5, 0, 6, 7], [0, 0, 8, 9, 0]],
...                dtype='int32')
>>> A.toarray()
array([[1, 2, 0, 0, 3],
       [4, 5, 0, 6, 7],
       [0, 0, 8, 9, 0]])
>>> tril(A).toarray()
array([[1, 0, 0, 0, 0],
       [4, 5, 0, 0, 0],
       [0, 0, 8, 0, 0]])
>>> tril(A).nnz
4
>>> tril(A, k=1).toarray()
array([[1, 2, 0, 0, 0],
       [4, 5, 0, 0, 0],
       [0, 0, 8, 9, 0]])
>>> tril(A, k=-1).toarray()
array([[0, 0, 0, 0, 0],
       [4, 0, 0, 0, 0],
       [0, 0, 0, 0, 0]])
>>> tril(A, format='csc')
<3x5 sparse matrix of type '<class 'numpy.int32'>'
        with 4 stored elements in Compressed Sparse Column format>
triu(A, k=0, format=None)

Return the upper triangular portion of a matrix in sparse format

Returns the elements on or above the k-th diagonal of the matrix A.
  • k = 0 corresponds to the main diagonal
  • k > 0 is above the main diagonal
  • k < 0 is below the main diagonal
A : dense or sparse matrix
Matrix whose upper trianglar portion is desired.
k : integer : optional
The bottom-most diagonal of the upper triangle.
format : string
Sparse format of the result, e.g. format=”csr”, etc.
L : sparse matrix
Upper triangular portion of A in sparse format.

tril : lower triangle in sparse format

>>> from scipy.sparse import csr_matrix, triu
>>> A = csr_matrix([[1, 2, 0, 0, 3], [4, 5, 0, 6, 7], [0, 0, 8, 9, 0]],
...                dtype='int32')
>>> A.toarray()
array([[1, 2, 0, 0, 3],
       [4, 5, 0, 6, 7],
       [0, 0, 8, 9, 0]])
>>> triu(A).toarray()
array([[1, 2, 0, 0, 3],
       [0, 5, 0, 6, 7],
       [0, 0, 8, 9, 0]])
>>> triu(A).nnz
8
>>> triu(A, k=1).toarray()
array([[0, 2, 0, 0, 3],
       [0, 0, 0, 6, 7],
       [0, 0, 0, 9, 0]])
>>> triu(A, k=-1).toarray()
array([[1, 2, 0, 0, 3],
       [4, 5, 0, 6, 7],
       [0, 0, 8, 9, 0]])
>>> triu(A, format='csc')
<3x5 sparse matrix of type '<class 'numpy.int32'>'
        with 8 stored elements in Compressed Sparse Column format>
_masked_coo(A, mask)