from __future__ import print_function, division

from sympy import SparseMatrix


def _doktocsr(dok):
    """Converts a sparse matrix to Compressed Sparse Row (CSR) format.

    Parameters
    ==========

    A : contains non-zero elements sorted by key (row, column)
    JA : JA[i] is the column corresponding to A[i]
    IA : IA[i] contains the index in A for the first non-zero element
        of row[i]. Thus IA[i+1] - IA[i] gives number of non-zero
        elements row[i]. The length of IA is always 1 more than the
        number of rows in the matrix.
    """
    row, JA, A = [list(i) for i in zip(*dok.row_list())]
    IA = [0]*((row[0] if row else 0) + 1)
    for i, r in enumerate(row):
        IA.extend([i]*(r - row[i - 1]))  # if i = 0 nothing is extended
    IA.extend([len(A)]*(dok.rows - len(IA) + 1))
    shape = [dok.rows, dok.cols]
    return [A, JA, IA, shape]


def _csrtodok(csr):
    """Converts a CSR representation to DOK representation"""
    smat = {}
    A, JA, IA, shape = csr
    for i in range(len(IA) - 1):
        indices = slice(IA[i], IA[i + 1])
        for l, m in zip(A[indices], JA[indices]):
            smat[i, m] = l
    return SparseMatrix(*(shape + [smat]))
