es sorting algorithms used.
    lexsort : Indirect stable sort with multiple keys.
    ndarray.sort : Inplace sort.
    argpartition : Indirect partial sort.
    take_along_axis : Apply ``index_array`` from argsort
                      to an array as if by calling sort.

    Notes
    -----
    See `sort` for notes on the different sorting algorithms.

    As of NumPy 1.4.0 `argsort` works with real/complex arrays containing
    nan values. The enhanced sort order is documented in `sort`.

    Examples
    --------
    One dimensional array:

    >>> x = np.array([3, 1, 2])
    >>> np.argsort(x)
    array([1, 2, 0])

    Two-dimensional array:

    >>> x = np.array([[0, 3], [2, 2]])
    >>> x
    array([[0, 3],
           [2, 2]])

    >>> ind = np.argsort(x, axis=0)  # sorts along first axis (down)
    >>> ind
    array([[0, 1],
           [1, 0]])
    >>> np.take_along_axis(x, ind, axis=0)  # same as np.sort(x, axis=0)
    array([[0, 2],
           [2, 3]])

    >>> ind = np.argsort(x, axis=1)  # sorts along last axis (across)
    >>> ind
    array([[0, 1],
           [0, 1]])
    >>> np.take_along_axis(x, ind, axis=1)  # same as np.sort(x, axis=1)
    array([[0, 3],
           [2, 2]])

    Indices of the sorted elements of a N-dimensional array:

    >>> ind = np.unravel_index(np.argsort(x, axis=None), x.shape)
    >>> ind
    (array([0, 1, 1, 0]), array([0, 0, 1, 1]))
    >>> x[ind]  # same as np.sort(x, axis=None)
    array([0, 2, 2, 3])

    Sorting with keys:

    >>> x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])
    >>> x
    array([(1, 0), (0, 1)],
          dtype=[('x', '<i4'), ('y', '<i4')])

    >>> np.argsort(x, order=('x','y'))
    array([1, 0])

    >>> np.argsort(x, order=('y','x'))
    array([0, 1])

    r