# -*- coding: utf-8 -*-
"""
Created on Mon Sep 29 16:14:17 2025

@author: 23749
"""


import numpy as np
from scipy.signal import butter, filtfilt

## 1.Bandpass Filter
def butter_bandpass(lowcut, highcut, fs, order=4):
    # 滤波器
    nyq = 0.5 * fs              #ny:Nyquist频率，即能表示的最大有效频率
    low = lowcut / nyq
    high = highcut / nyq
    b, a = butter(order, [low, high], btype='band')   #巴特沃斯滤波器，order=4阶
    return b, a

def bandpass_filter(data, lowcut, highcut, fs, order=4):
    b, a = butter_bandpass(lowcut, highcut, fs, order)    
    return filtfilt(b, a, data)

## 2.Eye Blink Dectection
def blink_detection(F, fs, Dmin, Dmax, Emin, Emax):
    """
    波形检测
    输入: 差分特征向量 F, 采样率 fs
    输出: b (0/1), 以及计算出的 d, e
    """

    if F is None or len(F) < 3:
        return 0, None, None

    # 找最大时间(peak) & 最小时间(valley)
    t_peak = np.argmax(F)
    t_valley = np.argmin(F)

    # 要求 peak 在 valley 之前（符合 blink 形态），否则交换
    if t_valley < t_peak:
        t_peak, t_valley = t_valley, t_peak

    # 计算持续时间 d (ms)
    d = (t_valley - t_peak) * 1000.0 / fs

    # 计算能量 e (差分平方和)
    e = np.sum(F[t_peak:t_valley + 1] ** 2)

    # 阈值判定
    if Dmin <= d <= Dmax and Emin <= e <= Emax:
        b = 1  # 检测到眨眼
    else:
        b = 0  # 否则 no blink
    return b, d, e

if __name__ == '__main__':
    import matplotlib.pyplot as plt
    
    fs = 250  # 采样率
    t = np.arange(0, 5, 1/fs)
    eog = 0.01 * np.random.randn(len(t))  # 基线+噪声
    
    # 模拟眨眼（在 2.0s 注入脉冲）
    center = int(2.0 * fs)
    eog[center:center+5] += 0.5
    eog[center+5:center+15] -= 0.4
    
    # 测试 blink_detection
    F = np.diff(eog)
    b, d, e = blink_detection(F, fs, 70, 500, 0.1, 10)
    print(f"Detected: {b}, Duration: {d}ms, Energy: {e}")