# -*- coding: utf-8 -*-
"""
Copyright Sylvain Bouveret, Yann Chevaleyre and François Durand
sylvain.bouveret@imag.fr, yann.chevaleyre@dauphine.fr, fradurand@gmail.com

This file is part of Whalrus.

Whalrus is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

Whalrus is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with Whalrus.  If not, see <http://www.gnu.org/licenses/>.
"""
from whalrus.rules.rule import Rule
from whalrus.rules.rule_iterated_elimination import RuleIteratedElimination
from whalrus.rules.rule_borda import RuleBorda
from whalrus.eliminations.elimination import Elimination
from whalrus.eliminations.elimination_below_average import EliminationBelowAverage


class RuleNanson(RuleIteratedElimination):
    """
    Nanson's rule.

    At each round, all candidates whose Borda score is lower than the average Borda score are eliminated.

    Parameters
    ----------
    args
        Cf. parent class.
    base_rule : Rule
        Default: :class:`RuleBorda`.
    elimination : Elimination
        Default: :class:`EliminationBelowAverage`.
    kwargs
        Cf. parent class.

    Examples
    --------
        >>> rule = RuleNanson(['a > b > c > d', 'a > b > d > c'])
        >>> rule.eliminations_[0].rule_.gross_scores_
        {'a': 6, 'b': 4, 'c': 1, 'd': 1}
        >>> rule.eliminations_[1].rule_.gross_scores_
        {'a': 2, 'b': 0}
        >>> rule.eliminations_[2].rule_.gross_scores_
        {'a': 0}
        >>> rule.winner_
        'a'
    """

    def __init__(self, *args, base_rule: Rule = None, elimination: Elimination = None, **kwargs):
        if base_rule is None:
            base_rule = RuleBorda()
        if elimination is None:
            elimination = EliminationBelowAverage()
        super().__init__(*args, base_rule=base_rule, elimination=elimination, **kwargs)
