accuracy converter

import scipy.stats as stats
from scipy.optimize import fsolve

# 全局变量定义
MALODY_RULES = {
    'A': {'BEST': 52, 'COOL': 92, 'GOOD': 126, 'MISS': 150},
    'B': {'BEST': 44, 'COOL': 84, 'GOOD': 118, 'MISS': 150},
    'C': {'BEST': 36, 'COOL': 76, 'GOOD': 110, 'MISS': 150},
    'D': {'BEST': 28, 'COOL': 68, 'GOOD': 102, 'MISS': 150},
    'E': {'BEST': 20, 'COOL': 60, 'GOOD': 94, 'MISS': 150},
}

OM_THRESHOLDS_TEMPLATE = {
    '300': lambda OD: 64 - 3 * OD,
    '200': lambda OD: 97 - 3 * OD,
    '100': lambda OD: 127 - 3 * OD,
    '50': lambda OD: 151 - 3 * OD,
}

OM_ACC_VALUES = {
    '300': 1.00,
    '200': 2 / 3,
    '100': 1 / 3,
    '50': 1 / 6,
}

def calculate_malody_stddev(acc, rule):
    def calculate_accuracy(stddev):
        p_best = stats.norm.cdf(rule['BEST'], 0, stddev) - stats.norm.cdf(-rule['BEST'], 0, stddev)
        p_cool = stats.norm.cdf(rule['COOL'], 0, stddev) - stats.norm.cdf(-rule['COOL'], 0, stddev) - p_best
        p_good = stats.norm.cdf(rule['GOOD'], 0, stddev) - stats.norm.cdf(-rule['GOOD'], 0, stddev) - p_cool - p_best

        total_accuracy = (p_best * 1.00 +
                          p_cool * 0.75 +
                          p_good * 0.40)
        return total_accuracy

    stddev = fsolve(lambda x: calculate_accuracy(x) - acc, 20 , xtol=1e-6)[0]

    return stddev

def calculate_om_stddev(acc, OD):
    thresholds = {key: func(OD) for key, func in OM_THRESHOLDS_TEMPLATE.items()}

    def calculate_accuracy(stddev):
        p_300 = stats.norm.cdf(thresholds['300'], 0, stddev) - stats.norm.cdf(-thresholds['300'], 0, stddev)
        p_200 = stats.norm.cdf(thresholds['200'], 0, stddev) - stats.norm.cdf(-thresholds['200'], 0, stddev) - p_300
        p_100 = stats.norm.cdf(thresholds['100'], 0, stddev) - stats.norm.cdf(-thresholds['100'], 0, stddev) - p_200 - p_300
        p_50 = stats.norm.cdf(thresholds['50'], 0, stddev) - stats.norm.cdf(-thresholds['50'], 0, stddev) - p_100 - p_200 - p_300

        total_accuracy = (p_300 * OM_ACC_VALUES['300'] +
                          p_200 * OM_ACC_VALUES['200'] +
                          p_100 * OM_ACC_VALUES['100'] +
                          p_50 * OM_ACC_VALUES['50'])
        return total_accuracy

    stddev = fsolve(lambda x: calculate_accuracy(x) - acc, 20, xtol=1e-6)[0]

    return stddev

def calculate_malody_acc(stddev, rule):
    p_best = stats.norm.cdf(rule['BEST'], 0, stddev) - stats.norm.cdf(-rule['BEST'], 0, stddev)
    p_cool = stats.norm.cdf(rule['COOL'], 0, stddev) - stats.norm.cdf(-rule['COOL'], 0, stddev) - p_best
    p_good = stats.norm.cdf(rule['GOOD'], 0, stddev) - stats.norm.cdf(-rule['GOOD'], 0, stddev) - p_cool - p_best

    total_accuracy = (p_best * 1.00 +
                      p_cool * 0.75 +
                      p_good * 0.40)
    return total_accuracy

def calculate_om_acc(stddev, OD):
    thresholds = {key: func(OD) for key, func in OM_THRESHOLDS_TEMPLATE.items()}

    p_300 = stats.norm.cdf(thresholds['300'], 0, stddev) - stats.norm.cdf(-thresholds['300'], 0, stddev)
    p_200 = stats.norm.cdf(thresholds['200'], 0, stddev) - stats.norm.cdf(-thresholds['200'], 0, stddev) - p_300
    p_100 = stats.norm.cdf(thresholds['100'], 0, stddev) - stats.norm.cdf(-thresholds['100'], 0, stddev) - p_200 - p_300
    p_50 = stats.norm.cdf(thresholds['50'], 0, stddev) - stats.norm.cdf(-thresholds['50'], 0, stddev) - p_100 - p_200 - p_300

    total_accuracy = (p_300 * OM_ACC_VALUES['300'] +
                      p_200 * OM_ACC_VALUES['200'] +
                      p_100 * OM_ACC_VALUES['100'] +
                      p_50 * OM_ACC_VALUES['50'])
    return total_accuracy

# 主程序入口
def main():
    print("请选择功能：")
    print("1: M2O：输入 Malody 判定、Malody ACC 和 OD，计算 O!M 的 ACC")
    print("2: O2M：输入 OD 和 O!M 的 ACC，计算 Malody 的 ACC")
    print("3: M2M：在不同 Malody 判定之间转换 ACC")
    print("4: O2O：在不同 O!M 判定之间转换 ACC")
    choice = int(input("请输入选择 (1/2/3/4): "))

    if choice == 1:
        print("可用的 Malody 判定规则：A, B, C, D, E")
        rule_choice = input("请选择 Malody 判定规则（A/B/C/D/E）：").strip().upper()
        if rule_choice not in MALODY_RULES:
            print("无效的规则选择，请选择 A, B, C, D, 或 E。")
            return

        malody_acc = float(input("请输入 Malody 的准确率 (例如 0.95)："))
        OD = float(input("请输入 OD 值（例如 9.2）："))

        rule = MALODY_RULES[rule_choice]
        stddev = calculate_malody_stddev(malody_acc, rule)
        om_acc = calculate_om_acc(stddev, OD)

        print(f"标准差为: {stddev:.2f} ms")
        print(f"O!M 准确率为: {om_acc:.4f}")

    elif choice == 2:
        OD = float(input("请输入 OD 值（例如 9.2）："))
        om_acc = float(input("请输入 O!M 的准确率 (例如 0.96)："))

        stddev = calculate_om_stddev(om_acc, OD)

        print("可用的 Malody 判定规则：A, B, C, D, E")
        rule_choice = input("请选择 Malody 判定规则（A/B/C/D/E）：").strip().upper()
        if rule_choice not in MALODY_RULES:
            print("无效的规则选择，请选择 A, B, C, D, 或 E。")
            return

        rule = MALODY_RULES[rule_choice]
        malody_acc = calculate_malody_acc(stddev, rule)

        print(f"标准差为: {stddev:.2f} ms")
        print(f"Malody 准确率为: {malody_acc:.4f}")

    elif choice == 3:
        print("可用的 Malody 判定规则：A, B, C, D, E")
        from_rule = input("请选择源 Malody 判定规则（A/B/C/D/E）：").strip().upper()
        to_rule = input("请选择目标 Malody 判定规则（A/B/C/D/E）：").strip().upper()
        if from_rule not in MALODY_RULES or to_rule not in MALODY_RULES:
            print("无效的规则选择，请选择 A, B, C, D, 或 E。")
            return

        malody_acc = float(input("请输入源 Malody 的准确率 (例如 0.95)："))
        stddev = calculate_malody_stddev(malody_acc, MALODY_RULES[from_rule])
        target_acc = calculate_malody_acc(stddev, MALODY_RULES[to_rule])

        print(f"目标规则 {to_rule} 下的准确率为: {target_acc:.4f}")

    elif choice == 4:
        from_acc = float(input("请输入源 O!M 的准确率 (例如 0.96)："))
        OD = float(input("请输入源OD 值（例如 9.2）："))
        stddev = calculate_om_stddev(from_acc, OD)
        to_OD = float(input("请输入目标OD 值（例如 9.2）："))
        to_acc = calculate_om_acc(stddev, to_OD)
        print(f"目标 O!M 的准确率为: {to_acc:.4f}")

# 运行主程序
if __name__ == "__main__":
    main()
    input("程序执行完毕，按任意键退出...")