from common.scripts import read_json_file
from rating.scripts.common import *


def evaluation_results(param):
    """
    评价结果
    清洗数据库数据，根据需要返回
    Parameters:
        param: 数据库数据
    Returns:
        credit_rank:  信用等级
        rank_date:  评价时间
    """
    data = param['评价结果']
    result = list()
    part_01 = dict()
    part_02 = dict()
    part_01['credit_rank'] = data['信用等级']
    part_02['rank_date'] = data['评价时间']
    result.append(part_01)
    result.append(part_02)
    return result


def financial_data(param):
    """
    主要财务数据
    清洗数据库数据，根据需要返回
    Parameters:
        param: 数据库数据
    Returns:
        三年主要财务数据
    """
    data_01 = param['综合评价填报']['财务填报']
    data_02 = param['指标明细']
    result = list()
    for key, value in data_01['资产负债表'][0].items():
        insert_list = list()
        insert_list.append(key)
        insert_list.append(value)
        insert_list.append(data_01['资产负债表'][1][key])
        insert_list.append(data_01['资产负债表'][2][key])
        insert_list = judge_list(insert_list)
        result.append(insert_list)
    for key, value in data_01['利润表'][0].items():
        if key != '报告期':
            insert_list = list()
            insert_list.append(key)
            insert_list.append(value)
            insert_list.append(data_01['利润表'][1][key])
            insert_list.append(data_01['利润表'][2][key])
            insert_list = judge_list(insert_list)
            result.append(insert_list)
    for k, v in data_02[0].items():
        if k == '已获利息倍数' or k == '总资产周转率' or k == '技术投入比率' or k == '速动比率':
            try:
                insert_list = list()
                insert_list.append(k)
                insert_list.append(v)
                insert_list.append(data_02[1][k])
                insert_list.append(data_02[2][k])
                insert_list = judge_list(insert_list)
                result.append(insert_list)
            except IndexError:
                insert_list = list()
                insert_list.append(k)
                insert_list.append(str(v))
                insert_list.append(str(data_02[1][k]))
                insert_list.append('-')
                result.append(insert_list)

    return result


def evaluation_view(param1, param2, param3):
    """
    评价观点 区分行业
    Parameters:
        param1: 企业名称
        param2: 数据库数据
        param3: 行业
    """
    if param3[0] == '制造业':
        return evaluation_view_manufacturing(param1, param2)
    elif param3[0] == '信息技术业':
        return evaluation_view_information(param1, param2)


def evaluation_view_manufacturing(param1, param2):
    """
    评价观点 制造业
    清洗数据库数据，根据需要返回
    Parameters:
        param1: 企业名称
        param2: 数据库数据
    Returns:
        评价观点数据
    """
    # Params
    data_01 = param2['基本信息']['基本信息']
    data_02 = param2['综合评价填报']['财务填报']
    data_03 = param2['评价结果']

    # Returns
    result = list()

    def para_01():
        part_01 = dict()
        # 企业名称
        part_01['company'] = param1
        # 省份
        part_01['province'] = data_01['regLocation'][:2]

        # 规模
        def scale():
            income = float(data_02['利润表'][0]['营业收入'])
            # 企业规模
            if income > 40000:
                part_01['scale'] = '大型'
            elif income > 2000:
                part_01['scale'] = '中型'
            elif income > 300:
                part_01['scale'] = '小型'
            else:
                part_01['scale'] = '微型'

        # 优势领域
        def advantage_area():
            advantage_list = list()
            if float(data_02['资产负债表'][0]['资产总计']) > 400000000:
                advantage_list.append('资产规模')
            elif float(data_02['补充数据表'][0]['研发费用']) / float(data_02['利润表'][0]['营业收入'] * 100) > 10:
                advantage_list.append('研发实力')
            elif float(data_02['资产负债表'][0]['短期借款']) != 0 or float(data_02['资产负债表'][0]['一年内到期非流动负债']) != 0 or float(
                    data_02['资产负债表'][0]['长期借款']) != 0:
                advantage_list.append('融资渠道')
            part_01['advantage_area'] = '、'.join(advantage_list)
        # 执行子方法
        scale()
        advantage_area()
        return part_01

    def para_02():
        part_02 = dict()

        # esg相关描述
        def esg_desc():
            esg_score = float(data_03['经营评分']['合计'])
            esg_desc_01 = '公司经营符合ESG理念，为环境友好型企业，承担相应社会责任，公司治理较好，为企业运营奠定良好基础。'
            esg_desc_02 = '公司正努力践行ESG理念，实行节能减排措施，积极履行社会责任，改善公司治理状况，公司ESG的提升有望为企业创造更多价值。'
            esg_desc_03 = '公司ESG理念较为薄弱，在经营过程中需增加节能减排措施，加大对供应链、社会公众、员工和地区等的责任，树立良好企业形象，进一步加强公司治理和内部控制，促进经营效率提升和企业价值创造。'
            if esg_score >= 24:
                part_02['esg_desc'] = esg_desc_01
            elif esg_score >= 18:
                part_02['esg_desc'] = esg_desc_02
            else:
                part_02['esg_desc'] = esg_desc_03

        esg_desc()
        return part_02

    def para_03():
        part_03 = dict()

        # 盈利能力
        def profitability():
            profitability_score = float(data_03['财务评分']['盈利能力']['净资产收益率']) + float(data_03['财务评分']['盈利能力']['总资产报酬率'])
            if profitability_score >= 16:
                part_03['profitability'] = '好'
            elif profitability_score >= 16 * 0.8:
                part_03['profitability'] = '较好'
            elif profitability_score >= 16 * 0.6:
                part_03['profitability'] = '处于行业平均水平'
            elif profitability_score >= 16 * 0.4:
                part_03['profitability'] = '较差'
            else:
                part_03['profitability'] = '差'

        # 资产运营效率
        def operational_efficiency():
            operation_score = float(data_03['财务评分']['资产质量']['总资产周转率']) + float(
                data_03['财务评分']['资产质量']['应收账款周转率']) + float(
                data_03['财务评分']['资产质量']['存货周转率'])
            if operation_score >= 18:
                part_03['operational_efficiency'] = '高'
            elif operation_score >= 18 * 0.8:
                part_03['operational_efficiency'] = '较高'
            elif operation_score >= 18 * 0.6:
                part_03['operational_efficiency'] = '尚可'
            elif operation_score >= 18 * 0.4:
                part_03['operational_efficiency'] = '较低'
            else:
                part_03['operational_efficiency'] = '低'

        # 债务风险
        def debt_risk():
            debt_risk_score = float(data_03['财务评分']['债务风险']['资产负债率']) + float(
                data_03['财务评分']['债务风险']['已获利息倍数']) + float(
                data_03['财务评分']['债务风险']['速动比率'])
            if debt_risk_score >= 18:
                part_03['debt_risk'] = '大'
            elif debt_risk_score >= 18 * 0.8:
                part_03['debt_risk'] = '较大'
            elif debt_risk_score >= 18 * 0.6:
                part_03['debt_risk'] = '适中'
            elif debt_risk_score >= 18 * 0.4:
                part_03['debt_risk'] = '较小'
            else:
                part_03['debt_risk'] = '小'

        # 成长能力
        def growth_ability():
            growth_ability_score = float(data_03['财务评分']['经营增长']['营业增长率']) + float(
                data_03['财务评分']['经营增长']['总资产增长率']) + float(
                data_03['财务评分']['经营增长']['技术投入比率'])
            if growth_ability_score >= 18:
                part_03['growth_ability'] = '强'
            elif growth_ability_score >= 18 * 0.8:
                part_03['growth_ability'] = '较强'
            elif growth_ability_score >= 18 * 0.6:
                part_03['growth_ability'] = '一般'
            elif growth_ability_score >= 18 * 0.4:
                part_03['growth_ability'] = '较弱'
            else:
                part_03['growth_ability'] = '弱'

        # 整体信用风险
        def overall_credit_risk():
            overall_credit_score = data_03['信用等级']
            if overall_credit_score == 'AAA' or overall_credit_score == 'AA':
                part_03['overall_credit_risk'] = '很小'
            elif overall_credit_score == 'A':
                part_03['overall_credit_risk'] = '小'
            elif overall_credit_score == 'BBB':
                part_03['overall_credit_risk'] = '可控'
            elif overall_credit_score == 'BB' or overall_credit_score == 'B':
                part_03['overall_credit_risk'] = '较大'
            else:
                part_03['overall_credit_risk'] = '大'

        profitability()
        operational_efficiency()
        debt_risk()
        growth_ability()
        overall_credit_risk()
        return part_03

    def para_04():
        part_04 = dict()
        part_04['company'] = param1
        part_04['credit_rank'] = data_03['信用等级']
        return part_04

    result.append(para_01())
    result.append(para_02())
    result.append(para_03())
    result.append(para_04())
    return result


def evaluation_view_information(param1, param2):
    """
    评价观点 信息技术业
    清洗数据库数据，根据需要返回
    Parameters:
        param1: 企业名称
        param2: 数据库数据
    Returns:
        评价观点数据
    """
    # Params
    data_01 = param2['基本信息']['基本信息']
    data_02 = param2['综合评价填报']['财务填报']
    data_03 = param2['评价结果']

    # Returns
    result = list()

    def para_01():
        part_01 = dict()
        # 企业名称
        part_01['company'] = param1
        # 省份
        part_01['province'] = data_01['regLocation'][:2]
        # 二级行业
        industry = data_02['综合评价填报']['行业选择'][1]

        # 规模-计算机服务与软件业
        def scale_01():
            income = float(data_02['利润表'][0]['营业收入'])
            # 企业规模
            if income > 100000000:
                part_01['scale'] = '大型'
            elif income > 10000000:
                part_01['scale'] = '中型'
            elif income > 500000:
                part_01['scale'] = '小型'
            else:
                part_01['scale'] = '微型'

        # 规模-电信业
        def scale_02():
            income = float(data_02['利润表'][0]['营业收入'])
            # 企业规模
            if income > 100000000:
                part_01['scale'] = '大型'
            elif income > 10000000:
                part_01['scale'] = '中型'
            elif income > 500000:
                part_01['scale'] = '小型'
            else:
                part_01['scale'] = '微型'

        if industry == '计算机服务与软件业':
            scale_01()
        elif industry == '电信业':
            scale_02()

        # 优势领域
        def advantage_area():
            advantage_list = list()
            if float(data_02['资产负债表'][0]['资产总计']) > 10000000:
                advantage_list.append('资产规模')
            elif float(data_02['补充数据表'][0]['研发费用']) / float(data_02['利润表'][0]['营业收入'] * 100) > 8:
                advantage_list.append('研发实力')
            elif float(data_02['资产负债表'][0]['短期借款']) != 0 or float(data_02['资产负债表'][0]['一年内到期非流动负债']) != 0 or float(
                    data_02['资产负债表'][0]['长期借款']) != 0:
                advantage_list.append('融资渠道')
            part_01['advantage_area'] = '、'.join(advantage_list)

        # 执行子方法
        advantage_area()
        return part_01

    def para_02():
        part_02 = dict()

        # esg相关描述
        def esg_desc():
            esg_score = float(data_03['经营评分']['合计'])
            esg_desc_01 = '公司经营符合ESG理念，为环境友好型企业，承担相应社会责任，公司治理较好，为企业运营奠定良好基础。'
            esg_desc_02 = '公司正努力践行ESG理念，实行节能减排措施，积极履行社会责任，改善公司治理状况，公司ESG的提升有望为企业创造更多价值。'
            esg_desc_03 = '公司ESG理念较为薄弱，在经营过程中需增加节能减排措施，加大对供应链、社会公众、员工和地区等的责任，树立良好企业形象，进一步加强公司治理和内部控制，促进经营效率提升和企业价值创造。'
            if esg_score >= 24:
                part_02['esg_desc'] = esg_desc_01
            elif esg_score >= 18:
                part_02['esg_desc'] = esg_desc_02
            else:
                part_02['esg_desc'] = esg_desc_03

        esg_desc()
        return part_02

    def para_03():
        part_03 = dict()

        # 盈利能力
        def profitability():
            profitability_score = float(data_03['财务评分']['盈利能力']['净资产收益率']) + float(data_03['财务评分']['盈利能力']['总资产报酬率'])
            if profitability_score >= 16:
                part_03['profitability'] = '好'
            elif profitability_score >= 16 * 0.8:
                part_03['profitability'] = '较好'
            elif profitability_score >= 16 * 0.6:
                part_03['profitability'] = '处于行业平均水平'
            elif profitability_score >= 16 * 0.4:
                part_03['profitability'] = '较差'
            else:
                part_03['profitability'] = '差'

        # 资产运营效率
        def operational_efficiency():
            operation_score = float(data_03['财务评分']['资产质量']['总资产周转率']) + float(
                data_03['财务评分']['资产质量']['应收账款周转率']) + float(
                data_03['财务评分']['资产质量']['存货周转率'])
            if operation_score >= 18:
                part_03['operational_efficiency'] = '高'
            elif operation_score >= 18 * 0.8:
                part_03['operational_efficiency'] = '较高'
            elif operation_score >= 18 * 0.6:
                part_03['operational_efficiency'] = '尚可'
            elif operation_score >= 18 * 0.4:
                part_03['operational_efficiency'] = '较低'
            else:
                part_03['operational_efficiency'] = '低'

        # 债务风险
        def debt_risk():
            debt_risk_score = float(data_03['财务评分']['债务风险']['资产负债率']) + float(
                data_03['财务评分']['债务风险']['已获利息倍数']) + float(
                data_03['财务评分']['债务风险']['速动比率'])
            if debt_risk_score >= 18:
                part_03['debt_risk'] = '大'
            elif debt_risk_score >= 18 * 0.8:
                part_03['debt_risk'] = '较大'
            elif debt_risk_score >= 18 * 0.6:
                part_03['debt_risk'] = '适中'
            elif debt_risk_score >= 18 * 0.4:
                part_03['debt_risk'] = '较小'
            else:
                part_03['debt_risk'] = '小'

        # 成长能力
        def growth_ability():
            growth_ability_score = float(data_03['财务评分']['经营增长']['营业增长率']) + float(
                data_03['财务评分']['经营增长']['总资产增长率']) + float(
                data_03['财务评分']['经营增长']['技术投入比率'])
            if growth_ability_score >= 18:
                part_03['growth_ability'] = '强'
            elif growth_ability_score >= 18 * 0.8:
                part_03['growth_ability'] = '较强'
            elif growth_ability_score >= 18 * 0.6:
                part_03['growth_ability'] = '一般'
            elif growth_ability_score >= 18 * 0.4:
                part_03['growth_ability'] = '较弱'
            else:
                part_03['growth_ability'] = '弱'

        # 整体信用风险
        def overall_credit_risk():
            overall_credit_score = float(data_03['财务评分']['合计'])
            if overall_credit_score >= 100:
                part_03['overall_credit_risk'] = '很小'
            elif overall_credit_score >= 100 * 0.8:
                part_03['overall_credit_risk'] = '小'
            elif overall_credit_score >= 100 * 0.6:
                part_03['overall_credit_risk'] = '可控'
            elif overall_credit_score >= 100 * 0.4:
                part_03['overall_credit_risk'] = '较大'
            else:
                part_03['overall_credit_risk'] = '大'

        profitability()
        operational_efficiency()
        debt_risk()
        growth_ability()
        overall_credit_risk()
        return part_03

    def para_04():
        part_04 = dict()
        part_04['company'] = param1
        part_04['credit_rank'] = data_03['信用等级']
        return part_04

    result.append(para_01())
    result.append(para_02())
    result.append(para_03())
    result.append(para_04())
    return result


def attention(param):
    """
    关注
    Parameters:
        param: 数据库数据
    Returns:
        result: 关注结果
    """
    weight = read_json_file('/rating/static/attention/weights.json')
    describe = read_json_file('/rating/static/attention/describe.json')
    data = param['评价结果']['财务评分']
    del param['评价结果']['财务评分']['合计']
    for k, v in data.items():
        for k_1, v_1 in v.items():
            if k_1 != '合计':
                weight[k_1] = round(v_1 / weight[k_1], 2)
    res = sorted(weight.items(), key=lambda x: x[1])
    result = list()
    part_01 = dict()
    part_02 = dict()
    part_03 = dict()
    part_01['attention_01'] = describe[res[0][0]]
    part_02['attention_02'] = describe[res[1][0]]
    part_03['attention_03'] = describe[res[2][0]]
    result.append(part_01)
    result.append(part_02)
    result.append(part_03)
    return result