def arrange_rows(row_queue, t):
    """
    Takes a priority queue as generated by place_items_in_square().
    Arranges the items from its conceptual square to one list.  Returns
    both the resultant vector, plus the displacement vector, to be used
    in the final output hash function.

    >>> rows = [(2, 1, [(0, 1), (1, 5)]), (3, 3, [(1, 7)])]
    >>> result, displacements = arrange_rows(rows, 4)
    >>> result
    (1, 5, 7)
    >>> displacements
    (None, 0, None, 1)

    >>> rows = [(1, 1, [(0, 1), (2, 7)]), (2, 2, [(1, 5)])]
    >>> result, displacements = arrange_rows(rows, 3)
    >>> result
    (1, 5, 7)
    >>> displacements
    (None, 0, 0)
    """

    # Create a set of all of the unoccupied columns.
    max_columns = t ** 2
    cols = ((x, True) for x in range(max_columns))
    unoccupied_columns = collections.OrderedDict(cols)

    # Create the resultant and displacement vectors.
    result = [None] * max_columns
    displacements = [None] * t

    while row_queue:
        # Get the next row to place.
        _inverse_length, y, row = heapq.heappop(row_queue)

        offset = find_first_fit(unoccupied_columns, row, max_columns)
        # Calculate the offset of the first item.
        first_item_x = row[0][0]

        displacements[y] = offset
        for x, item in row:
            actual_x = x + offset
            result[actual_x] = item
            del unoccupied_columns[actual_x]

    return tuple(trim_nones_from_right(result)), tuple(displacements)
def arrange_rows ( row_queue , t ) : max_columns = t ** 2 cols = ( ( x , True ) for x in range ( max_columns ) ) unoccupied_columns = collections . OrderedDict ( cols ) result = [ None ] * max_columns displacements = [ None ] * t while row_queue : _inverse_length , y , row = heapq . heappop ( row_queue ) offset = find_first_fit ( unoccupied_columns , row , max_columns ) first_item_x = row [ 0 ] [ 0 ] displacements [ y ] = offset for x , item in row : actual_x = x + offset result [ actual_x ] = item del unoccupied_columns [ actual_x ] return tuple ( trim_nones_from_right ( result ) ) , tuple ( displacements )
Takes a priority queue as generated by place_items_in_square().
    Arranges the items from its conceptual square to one list.  Returns
    both the resultant vector, plus the displacement vector, to be used
    in the final output hash function.

    >>> rows = [(2, 1, [(0, 1), (1, 5)]), (3, 3, [(1, 7)])]
    >>> result, displacements = arrange_rows(rows, 4)
    >>> result
    (1, 5, 7)
    >>> displacements
    (None, 0, None, 1)

    >>> rows = [(1, 1, [(0, 1), (2, 7)]), (2, 2, [(1, 5)])]
    >>> result, displacements = arrange_rows(rows, 3)
    >>> result
    (1, 5, 7)
    >>> displacements
    (None, 0, 0)