package scipy

Get an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.

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

Parameters ---------- A : dense or sparse matrix Matrix whose nonzero elements are desired.

Returns ------- (I,J,V) : tuple of arrays I,J, and V contain the row indices, column indices, and values of the nonzero matrix entries.

Examples -------- >>> 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.))

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

Parameters ---------- 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.

Returns ------- L : sparse matrix Lower triangular portion of A in sparse format.

See Also -------- triu : upper triangle in sparse format

Examples -------- >>> 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>

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

Parameters ---------- 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.

Returns ------- L : sparse matrix Upper triangular portion of A in sparse format.

See Also -------- tril : lower triangle in sparse format

Examples -------- >>> 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>