csr_check

csr_check() def csr_check ( m  , n  , val  , rowptr  , colind  , result    ) Check sparse matrix (CSR) Purpose This routine checks the sparse matrix in CSR format. Use separate routine csx_check_sym to check symmetry. Returns info (int) = 0: Successful exit. = i < 0: The (-i)-th argument is invalid. = 1: Empty row(s) found.(*) = 2: Row(s) with unsorted column indices found.(*) = 4: Diagonal element(s) with zero values (val = 0) found.(*) = 8: Invalid column index(es) found.(*) (*) Sum of these codes will be returned (e.g. 15 = 1 + 2 + 4 + 8 indicates that all four cases were detected). = 16: The value of ptr[0] is invalid. = 17: The value of (ptr[m] - ptr[0]) is invalid. = 18: the value(s) of ptr[i](i > 0) are invalid. Parameters [in] m Number of rows of the matrix. (m >= 0) (if m = 0, returns without computation) [in] n Number of columns of the matrix. (n >= 0) (if n = 0, returns without computation) [in] val Numpy ndarray (1-dimensional, float, nnz) Values of nonzero elements of input matrix (where nnz is the number of nonzero elements). [in] rowptr Numpy ndarray (1-dimensional, int32, m + 1) Row pointers of input matrix. [in] colind Numpy ndarray (1-dimensional, int32, nnz) Column indices of input matrix (where nnz is the number of nonzero elements). [out] result Numpy ndarray (1-dimensional, int32, 11) Check result (valid for 0 <= info <= 15). result[0]: Indexing of rowptr and colind (0 or 1). result[1]: Number of nonzero elements. result[2]: Number of nonzero elements in lower triangular part. result[3]: Number of nonzero elements in upper triangular part. result[4]: Number of nonzero diagonal elements. result[5]: Number of elements with zero values (val[i] = 0). result[6]: Number of diagonal elements with zero values (val[i] = 0). result[7]: Number of empty rows. result(8): Number of rows with unsorted column indices. result[9]: Number of invaid column indices. result[10]: Symmetry (1 if symmetric matrix, 0 otherwise).