def profitability(param):
    """
    资质荣誉 区分行业
    Parameters:
        param: 数据库数据
    """
    industry = param['综合评价填报']['行业选择'][0]
    if industry == '制造业':
        return profitability_manufacturing(param)
    elif industry == '信息技术业':
        return profitability_infomation(param)


def profitability_manufacturing(param):
    """
    盈利能力-制造业
    清洗数据库数据，根据需要返回
    Parameters:
        param: 数据库数据
    Returns:
        盈利能力数据
    """
    # Params
    data_01 = param['综合评价填报']
    data_02 = param['评价结果']
    data_03 = param['指标明细']

    # Returns
    result = list()
    part_01 = dict()

    def para_02():
        part_02 = dict()
        # 年度
        part_02['date'] = data_01['财务填报']['利润表'][1]['报告期'][:4]
        # 净资产收益率
        part_02['return_on_net_assets_analysis'] = str(data_03[1]['净资产收益率'])
        # 总资产报酬率
        part_02['return_on_total_assets_analysis'] = str(data_03[1]['总资产报酬率'])

        def income_scale():
            # 收入规模
            income = float(data_01['财务填报']['利润表'][0]['营业收入'])
            if income > 400000000:
                part_02['income_scale'] = '较大'
            elif income > 20000000:
                part_02['income_scale'] = '处于行业中等水平'
            elif income > 3000000:
                part_02['income_scale'] = '较小'
            else:
                part_02['income_scale'] = '小'

        def technology_level():
            # 产品技术含量
            rona = data_02['财务评分']['盈利能力']['净资产收益率']
            if rona >= 8:
                part_02['technology_level'] = '高'
            elif rona >= 8 * 0.8:
                part_02['technology_level'] = '较高'
            elif rona >= 8 * 0.6:
                part_02['technology_level'] = '处于行业平均水平'
            elif rona >= 8 * 0.4:
                part_02['technology_level'] = '较低'
            else:
                part_02['technology_level'] = '低'

        def profitability_analysis():
            # 盈利能力
            p_score = data_02['财务评分']['盈利能力']['净资产收益率'] + data_02['财务评分']['盈利能力']['总资产报酬率']
            if p_score >= 16:
                part_02['profitability_analysis'] = '好'
            elif p_score >= 16 * 0.8:
                part_02['profitability_analysis'] = '较好'
            elif p_score >= 16 * 0.8:
                part_02['profitability_analysis'] = '有待提高'
            elif p_score >= 16 * 0.8:
                part_02['profitability_analysis'] = '较弱'
            else:
                part_02['profitability_analysis'] = '弱'

        income_scale()
        technology_level()
        profitability_analysis()
        return part_02

    result.append(part_01)
    result.append(para_02())
    return result


def profitability_infomation(param):
    """
    盈利能力-信息技术业
    清洗数据库数据，根据需要返回
    Parameters:
        param: 数据库数据
    Returns:
        盈利能力数据
    """
    # Params
    data_01 = param['综合评价填报']
    data_03 = param['指标明细']

    # Returns
    result = list()
    part_01 = dict()
    part_02 = dict()

    def para_03():
        part_03 = dict()
        # 年度
        part_03['date'] = data_01['财务填报']['利润表'][1]['报告期'][:4]
        # 净资产收益率
        part_03['return_on_net_assets_analysis'] = str(data_03[1]['净资产收益率'])
        try:
            # 是否高于平均水平
            if data_03[1]['净资产收益率'] > 4.7:
                part_03['high_low_01'] = '高于'
            else:
                part_03['high_low_01'] = '低于'
        except TypeError:
            part_03['high_low_01'] = '低于'
        # 总资产报酬率
        part_03['return_on_total_assets_analysis'] = str(data_03[1]['总资产报酬率'])
        try:
            # 是否高于平均水平
            if data_03[1]['总资产报酬率'] > 2.7:
                part_03['high_low_02'] = '高于'
            else:
                part_03['high_low_02'] = '低于'
        except TypeError:
            part_03['high_low_02'] = '低于'
        return part_03

    result.append(part_01)
    result.append(part_02)
    result.append(para_03())
    return result


def asset_quality(param):
    """
    资产质量 区分行业
    Parameters:
        param: 数据库数据
    """
    industry = param['综合评价填报']['行业选择'][0]
    if industry == '制造业':
        return asset_quality_manufacturing(param)
    elif industry == '信息技术业':
        return asset_quality_infomation(param)


def asset_quality_manufacturing(param):
    """
    资产质量-制造业
    清洗数据库数据，根据需要返回
    Parameters:
        param: 数据库数据
    Returns:
        资产质量数据
    """
    # Params
    data_01 = param['综合评价填报']
    data_02 = param['评价结果']
    data_03 = param['指标明细']

    # Returns
    result = list()
    part_01 = dict()
    part_02 = dict()

    def para_03():
        part_03 = dict()
        # 年度
        part_03['date'] = data_01['财务填报']['利润表'][1]['报告期'][:4]
        # 应收账款周转率
        part_03['account_turnover_analysis'] = str(data_03[0]['应收账款周转率'])
        # 存货周转率
        part_03['inventory_turnover_analysis'] = str(data_03[0]['存货周转率'])
        # 总资产周转率
        part_03['total_assets_turnover_analysis'] = str(data_03[0]['总资产周转率'])

        def industry_asset():
            a = float(data_02['财务评分']['资产质量']['总资产周转率'])
            s = float(data_02['财务评分']['资产质量']['应收账款周转率'])
            c = float(data_02['财务评分']['资产质量']['存货周转率'])
            a_s = a + s + c
            if a_s >= 18:
                part_03['industry_level'] = '高'
                part_03['asset_quality'] = '好'
            elif a_s >= 18 * 0.8:
                part_03['industry_level'] = '较高'
                part_03['asset_quality'] = '较好'
            elif a_s >= 18 * 0.6:
                part_03['industry_level'] = '中等'
                part_03['asset_quality'] = '有待改善'
            elif a_s >= 18 * 0.4:
                part_03['industry_level'] = '偏低'
                part_03['asset_quality'] = '较差'
            else:
                part_03['industry_level'] = '低'
                part_03['asset_quality'] = '差'

        industry_asset()
        return part_03

    result.append(part_01)
    result.append(part_02)
    result.append(para_03())
    return result


def asset_quality_infomation(param):
    """
    资产质量-信息技术业
    清洗数据库数据，根据需要返回
    Parameters:
        param: 数据库数据
    Returns:
        资产质量数据
    """
    # Params
    data_01 = param['综合评价填报']
    data_02 = param['评价结果']
    data_03 = param['指标明细']

    # Returns
    result = list()

    def para_01():
        part_01 = dict()
        # 年度
        part_01['date'] = data_01['财务填报']['利润表'][1]['报告期'][:4]
        # 应收账款周转率
        part_01['account_turnover_analysis'] = str(data_03[1]['应收账款周转率'])
        try:
            # 是否高于平均水平
            if data_03[1]['应收账款周转率'] > 0.4:
                part_01['high_low_01'] = '高'
            else:
                part_01['high_low_01'] = '低'
        except TypeError:
            part_01['high_low_01'] = '低'
        # 总资产周转率
        part_01['total_assets_turnover_analysis'] = str(data_03[1]['总资产周转率'])
        try:
            # 是否高于平均水平
            if data_03[1]['总资产周转率'] > 3.9:
                part_01['high_low_02'] = '高于'
            else:
                part_01['high_low_02'] = '低于'
        except TypeError:
            part_01['high_low_02'] = '低于'

        return part_01

    result.append(para_01())
    return result


def debt_risk(param):
    """
    债务风险
    清洗数据库数据，根据需要返回
    Parameters:
        param: 数据库数据
    Returns:
        债务风险数据
    """
    # Params
    data_02 = param['评价结果']
    data_03 = param['指标明细']

    # Returns
    result = list()
    part_01 = dict()

    def para_02():
        part_02 = dict()

        def debt_risk_detail():
            a = float(data_02['财务评分']['债务风险']['资产负债率'])
            d = float(data_02['财务评分']['债务风险']['已获利息倍数'])
            s = float(data_02['财务评分']['债务风险']['速动比率'])
            d_s = a + d + s
            if d_s >= 18:
                part_02['debt_risk'] = '大'
            elif d_s >= 18 * 0.8:
                part_02['debt_risk'] = '较大'
            elif d_s >= 18 * 0.6:
                part_02['debt_risk'] = '适中'
            elif d_s >= 18 * 0.4:
                part_02['debt_risk'] = '较小'
            else:
                part_02['debt_risk'] = '小'

        def asset_liability_ratio():
            # 资产负债率区间
            if len(data_03) >= 3:
                rate = [data_03[0]['资产负债率'], data_03[1]['资产负债率'], data_03[2]['资产负债率']]
                rate.sort()
                part_02['min_asset_liability_ratio'] = str(rate[0])
                part_02['max_asset_liability_ratio'] = str(rate[2])
            else:
                rate_01 = data_03[0]['资产负债率']
                rate_02 = data_03[1]['资产负债率']
                if rate_01 > rate_02:
                    part_02['max_asset_liability_ratio'] = str(rate_01)
                    part_02['min_asset_liability_ratio'] = str(rate_02)
                else:
                    part_02['max_asset_liability_ratio'] = str(rate_02)
                    part_02['min_asset_liability_ratio'] = str(rate_01)

        def fin_structure_analysis():
            # 财务结构
            alr = data_03[0]['资产负债率']
            if alr < 0.4:
                part_02['fin_structure_analysis'] = '稳健'
            elif alr < 0.55:
                part_02['fin_structure_analysis'] = '较稳健'
            elif alr < 0.7:
                part_02['fin_structure_analysis'] = '稳健性有待进一步加强'
            else:
                part_02['fin_structure_analysis'] = '欠稳健'

        def operating_cash_flow_to_debt():
            # 经营性现金流对利息的保障程度
            ic_s = data_02['财务评分']['债务风险']['已获利息倍数']
            if ic_s >= 5:
                part_02['operating_cash_flow_to_debt'] = '强'
            elif ic_s >= 5 * 0.8:
                part_02['operating_cash_flow_to_debt'] = '较强'
            elif ic_s >= 5 * 0.6:
                part_02['operating_cash_flow_to_debt'] = '尚可'
            elif ic_s >= 5 * 0.4:
                part_02['operating_cash_flow_to_debt'] = '较弱'
            else:
                part_02['operating_cash_flow_to_debt'] = '弱'

        def assets_liquidity():
            # 资产流动性
            qr_s = data_02['财务评分']['债务风险']['速动比率']
            if qr_s >= 5:
                part_02['assets_liquidity'] = '好'
            elif qr_s >= 5 * 0.8:
                part_02['assets_liquidity'] = '较好'
            elif qr_s >= 5 * 0.6:
                part_02['assets_liquidity'] = '一般'
            elif qr_s >= 5 * 0.4:
                part_02['assets_liquidity'] = '较差'
            else:
                part_02['assets_liquidity'] = '差'

        # 已获利息倍数
        part_02['interest_cover'] = str(data_03[0]['已获利息倍数'])
        # 速动比率
        part_02['quick_ratio'] = str(data_03[0]['速动比率'])
        debt_risk_detail()
        asset_liability_ratio()
        asset_liability_ratio()
        fin_structure_analysis()
        operating_cash_flow_to_debt()
        assets_liquidity()
        return part_02

    result.append(part_01)
    result.append(para_02())
    return result


def growth_ability(param):
    """
    成长能力
    清洗数据库数据，根据需要返回
    Parameters:
        param: 数据库数据
    Returns:
        成长能力数据
    """
    # Params
    data_02 = param['评价结果']
    data_03 = param['指标明细']

    # Returns
    result = list()
    part_01 = dict()

    def para_02():
        part_02 = dict()

        def degree_of_imprtance():
            # 重视程度
            ti_s = data_02['财务评分']['经营增长']['技术投入比率']
            if ti_s >= 5 * 0.7:
                part_02['degree_of_imprtance'] = '重视技术研发或先进工艺的开发'
            elif ti_s >= 5 * 0.4:
                part_02['degree_of_imprtance'] = '对技术研发或先进工艺的开发的重视程度一般'
            else:
                part_02['degree_of_imprtance'] = '不重视技术研发或先进工艺的开发'

        # 技术投入比
        part_02['technology_input_ratio'] = str(data_03[1]['技术投入比率'])

        def contribution_growth_ability():
            g = float(data_02['财务评分']['经营增长']['营业增长率'])
            a = float(data_02['财务评分']['经营增长']['总资产增长率'])
            s = float(data_02['财务评分']['经营增长']['技术投入比率'])
            ga_s = g + a + s
            if ga_s >= 18:
                part_02['contribution'] = '大'
                part_02['growth_ability'] = '好'
            elif ga_s >= 18 * 0.8:
                part_02['contribution'] = '较大'
                part_02['growth_ability'] = '较好'
            elif ga_s >= 18 * 0.6:
                part_02['contribution'] = '尚可'
                part_02['growth_ability'] = '一般'
            elif ga_s >= 18 * 0.4:
                part_02['contribution'] = '较小'
                part_02['growth_ability'] = '较差'
            else:
                part_02['contribution'] = '小'
                part_02['growth_ability'] = '差'
        # 营业收入增长率
        part_02['growth_rate_of_operating_revenue'] = str(data_03[1]['营业增长率'])
        # 总资产增长率
        part_02['growth_rate_of_total_assets'] = str(data_03[1]['总资产增长率'])
        degree_of_imprtance()
        contribution_growth_ability()
        return part_02

    result.append(part_01)
    result.append(para_02())
    return result