import operator


def social_turnover_salary(param):
    """
    离职率/人均薪酬水平
    Parameters:
        param dict 数据库数据
    Returns:
        result dict 清洗好的数据
    """
    # Params
    three_year_data = sorted(param['社会问卷']['近三年公司数据'], key=operator.itemgetter('年份'), reverse=False)

    # Returns
    result = dict()

    def date_range():
        """
        评价周期年度区间
        Parameters:
            param dict 环境问卷
        Returns:
            result str 区间数据
        """
        year_begin = three_year_data[0]['年份']
        year_end = three_year_data[2]['年份']
        res = year_begin + '-' + year_end
        return res

    def turnover_ratio(year_param):
        """
        每年离职比例
        Parameters:
            param dict 年度社会问卷数据
        Returns:
            result type 该年度离职比例
        """
        # 离职率
        total = year_param['员工总数（年底）']
        turnover = year_param['当年离职人数']
        rate = round(turnover / total * 100, 2)
        return str(rate) + '%'

    def calculate_three_year_data():
        """
        计算三年数据并转为字符串
        Parameters:
            '-'
        Returns:
            result str 三年数据用顿号隔开
        """
        res = dict()
        year_turnover = list()
        yart_salary_level = list()
        for item in three_year_data:
            turnover_res = turnover_ratio(item)
            year_turnover.append(turnover_res)
            yart_salary_level.append(item['人均薪酬水平（元/月）'])
        # 列表中元素类型float转str
        yart_salary_level_new = [str(x) for x in yart_salary_level]

        # Returns-part1
        res['part_01'] = dict()
        res['part_01']['date_range'] = date_range()
        res['part_01']['turnover_ratio'] = '、'.join(year_turnover)
        res['part_01']['year_03'] = three_year_data[2]['年份']
        res['part_01']['industry_indicators'] = None
        res['part_01']['industry_indicators_average'] = None
        res['part_01']['industry_indicators_median'] = None
        res['part_01']['median_comparison'] = None

        # part2
        res['part_02'] = dict()
        res['part_02']['date_range'] = date_range()
        res['part_02']['salary_level'] = '、'.join(yart_salary_level_new)
        res['part_02']['year_02'] = three_year_data[1]['年份']
        res['part_02']['year_03'] = three_year_data[2]['年份']
        res['part_02']['year01_on_year02'] = round((yart_salary_level[1] / yart_salary_level[0] - 1) * 100, 2)
        res['part_02']['year02_on_year03'] = round((yart_salary_level[2] / yart_salary_level[1] - 1) * 100, 2)
        res['part_02']['industry_indicators'] = None
        res['part_02']['industry_indicators_average'] = None
        res['part_02']['industry_indicators_median'] = None
        res['part_02']['median_comparison'] = None

        return res

    result['part_01'] = calculate_three_year_data()['part_01']
    result['part_02'] = calculate_three_year_data()['part_02']

    return result


def social_labor_disputes(param):
    """
    劳动纠纷
    Parameters:
        param dict 数据库数据
    Returns:
        result dict 清洗好的数据
    """
    data = param['社会问卷']

    # Returns
    result = dict()

    for item in data['近三年公司数据']:
        if item['劳资纠纷次数'] > 0:
            result['describe'] = '劳动纠纷——公司在近三年出现了劳动纠纷，这影响了公司的社会得分。'
            break
        else:
            result['describe'] = False
    return result


def social_safety_accident(param):
    """
    安全事故
    Parameters:
        param dict 数据库数据
    Returns:
        result dict 清洗好的数据
    """
    data = param['社会问卷']

    # Returns
    result = dict()

    for item in data['近三年公司数据']:
        if item['安全事故发生次数'] > 0:
            result['describe'] = '安全事故——公司在近三年出现了安全事故，这影响了公司的社会得分。。'
            break
        else:
            result['describe'] = False
    return result


def social_training_return(param):
    """
    提供培训/产品返修、退还、投诉
    Parameters:
        param dict 数据库数据
    Returns:
        result dict 清洗好的数据
    """
    # Params
    three_year_data = sorted(param['社会问卷']['近三年公司数据'], key=operator.itemgetter('年份'), reverse=False)

    # Returns
    result = dict()

    def date_range():
        """
        评价周期年度区间
        Parameters:
            param dict 环境问卷
        Returns:
            result str 区间数据
        """
        year_begin = three_year_data[0]['年份']
        year_end = three_year_data[2]['年份']
        res = year_begin + '-' + year_end
        return res

    def calculate_three_year_data():
        """
        计算三年数据并转为字符串
        Parameters:
            '-'
        Returns:
            result str 三年数据用顿号隔开
        """
        res = dict()
        year_training = list()
        year_return = list()
        for item in three_year_data:
            year_training.append(str(item['组织培训次数']))
            year_return.append(str(item['返修、退回、投诉产品对应销售额占全部销售比例（%）']))

        # Returns-part1
        res['part_01'] = dict()
        res['part_01']['date_range'] = date_range()
        res['part_01']['training_number'] = '、'.join(year_training)
        res['part_01']['year_03'] = three_year_data[2]['年份']
        res['part_01']['industry_indicators'] = None

        # part2
        res['part_02'] = dict()
        res['part_02']['date_range'] = date_range()
        res['part_02']['salary_level'] = '、'.join(year_return)
        res['part_02']['year_03'] = three_year_data[2]['年份']
        res['part_02']['industry_indicators'] = None

        return res

    result['part_01'] = calculate_three_year_data()['part_01']
    result['part_02'] = calculate_three_year_data()['part_02']

    return result


def socail_poverty_alleviation(param):
    """
    扶贫和捐赠
    Parameters:
        param dict 数据库数据
    Returns:
        result dict 清洗好的数据
    """
    data = param['社会问卷']

    # Returns
    result = dict()

    for item in data['近三年公司数据']:
        if item['扶贫+捐赠规模（万元）'] > 0:
            result['describe'] = '扶贫和捐赠——公司在近三年有过扶贫、捐赠行为，这提高了公司社会得分。'
            break
        else:
            result['describe'] = False
    return result


def socail_legal_proceedings(param):
    """
    司法风险
    Parameters:
        param dict 数据库数据
    Returns:
        result dict 清洗好的数据
    """
    # Parameter
    data = param['风险数据']

    # Returns
    result = dict()

    for value in data.values():
        if value > 0:
            result['describe'] = '法律诉讼（作为被告）、被执行人、失信人、行政处罚、税收违法、严重违法、经营异常——经查，公司存在1项或多项的法律诉讼（作为被告）、被执行人、失信人、行政处罚、税收违法、严重违法、经营异常情况，这影响了公司社会分数。'
            break
        else:
            result['describe'] = False

    return result