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   源码介绍

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仅一个方法

import cmath
def psfeatureTime(data, p1, p2):
    df_min = data[p1:p2].min()  # 最小值

    df_max = data[p1:p2].max()  # 幅值

    df_mean = data[p1:p2].mean()  # 均值

    df_var = data[p1:p2].var()  # 方差

    df_std = data[p1:p2].std()  # 标准差

    df_rms = cmath.sqrt(pow(df_mean, 2)   pow(df_std, 2))  # 均方根

    # df_rms = cmath.sqrt(np.sum([x ** 2 for x in data[p1:p2]]) / len(data[p1:p2]))

    df_skew = Series(data[p1:p2]).skew()  # 偏度

    df_kurt = Series(data[p1:p2]).kurt()  # 峭度
    sum = 0
    for i in range(p1, p2):
        sum  = cmath.sqrt(abs(data[i]))

    df_s = df_rms / (abs(data[p1:p2]).mean())  # 波形因子

    df_c = (max(data[p1:p2])) / df_rms  # 峰值因子

    df_i = (max(data[p1:p2])) / (abs(data[p1:p2]).mean())  # 脉冲因子

    df_l = (max(data[p1:p2])) / pow((sum / (p2 - p1)), 2)  # 裕度因子

    timefeature_list = [df_min, df_max, df_mean, df_std, df_rms, df_skew, df_kurt, df_s, df_c, df_i, df_l]

    self.timeFeatureList = timefeature_list

    return timefeature_list