209 lines
8.2 KiB
Python
209 lines
8.2 KiB
Python
# -*-coding:utf-8 -*-
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"""
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数据滤波模块
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"""
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import numpy as np
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import time
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import threading
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from scipy import signal
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from logs.log import algo_log
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class FilterRingBuffer:
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def __init__(self, n_chan, n_points):
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"""
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初始化纯数据环形缓存（线程安全）
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:param n_chan: 通道数
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:param n_points: 总缓存点数（与paradigmRingBuffer参数完全一致）
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"""
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self.n_chan = n_chan
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self.n_points = n_points
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self.buffer = np.zeros((n_chan, n_points), dtype=np.float64)
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self.current_ptr = 0
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self.total_samples = 0
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self.lock = threading.Lock() # 仅保护元数据
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def appendBuffer(self, data):
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"""
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追加数据到缓存（与paradigmRingBuffer接口一致）
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:param data: 输入数据，shape=(n_chan, n_samples)
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"""
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n = data.shape[1]
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if n == 0:
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return
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# -------- 第一步：仅加锁读取/更新元数据（持锁极短）--------
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with self.lock:
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old_ptr = self.current_ptr
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new_ptr = (old_ptr + n) % self.n_points
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new_total = min(self.total_samples + n, self.n_points)
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# -------- 第二步：数组写入（耗时操作，移出锁外）--------
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write_end = old_ptr + n
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if write_end <= self.n_points:
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self.buffer[:, old_ptr:write_end] = data
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else:
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split = self.n_points - old_ptr
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self.buffer[:, old_ptr:] = data[:, :split]
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self.buffer[:, :write_end - self.n_points] = data[:, split:]
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# -------- 第三步：再次加锁更新最终元数据 --------
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with self.lock:
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self.current_ptr = new_ptr
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self.total_samples = new_total
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def getData(self, count):
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"""
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从最新位置向前读取count个点（环形读取）
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核心逻辑：current_ptr是下一个写入位置 → 最新数据在current_ptr之前
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:param count: 读取点数
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:return: np.ndarray, shape=(n_chan, count)
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"""
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# -------- 第一步：加锁获取最新元数据（持锁极短）--------
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with self.lock:
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count = min(count, self.total_samples)
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if count == 0:
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return np.zeros((self.n_chan, 0))
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end = self.current_ptr
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start = end - count
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if start >= 0:
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res = self.buffer[:, start:end].copy()
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else:
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part1 = self.buffer[:, start:]
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part2 = self.buffer[:, :end]
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res = np.concatenate((part1, part2), axis=1).copy()
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return res
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def get_latest_n_points(self, n):
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with self.lock:
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if self.total_samples < n:
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return None
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return self.getData(n)
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def GetDataLenCount(self):
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with self.lock:
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return self.total_samples
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def resetAllPara(self):
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with self.lock:
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self.buffer.fill(0.0)
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self.current_ptr = 0
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self.total_samples = 0
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# -----------------------------------------------------------------------------
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# 2. 独立滑动滤波类（仅负责滤波业务逻辑，不关心缓存实现）
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# -----------------------------------------------------------------------------
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class SlidingFilter(threading.Thread):
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def __init__(
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self,
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ring_buffer: FilterRingBuffer,
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n_chan=66,
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srate=250,
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window_sec=3,
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step_sec=0.2
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):
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super().__init__(daemon=True)
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# 核心参数
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self.n_chan = n_chan
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self.srate = srate
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self.step_sec = step_sec # 200ms滑动步长
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self.window_sec = window_sec # 3秒窗口
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self.step_sec = step_sec # 200ms滑动步长
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self.window_size = int(srate * window_sec) # 3秒点数：250*3=750
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self.step_size = int(srate * step_sec) # 200ms点数：250*0.2=50
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# 关联ZMQServer的环形缓存（解耦：仅依赖接口）
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self.ring_buffer = ring_buffer
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# 线程控制
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self.running = threading.Event()
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self.running.set()
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# 滤波结果回调（外部可注册，获取滤波后的数据）
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self.filter_result_callback = None
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# 预计算滤波器系数（仅执行一次）
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self._init_filters()
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def _init_filters(self):
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"""预计算所有滤波器系数（仅执行一次）"""
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# 50Hz工频陷波（Q=30，工业标准）
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self.b_notch, self.a_notch = signal.iirnotch(50, 30, self.srate)
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# 8~30Hz带通FIR（65阶，线性相位）
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self.b_bp = signal.firwin(
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numtaps=65,
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cutoff=[8/(self.srate/2), 30/(self.srate/2)],
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pass_zero=False,
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window='hamming'
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)
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self.a_bp = np.array([1.0])
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def _filter_window_data(self, window_data):
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"""对3秒窗口数据执行滤波，返回无边界效应的200ms数据"""
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# 零相位滤波（无延迟，无边界效应）
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filtered = window_data - np.mean(window_data, axis=-1, keepdims=True)
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filtered = signal.filtfilt(self.b_notch, self.a_notch, filtered, axis=-1)
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filtered = signal.filtfilt(self.b_bp, self.a_bp, filtered, axis=-1)
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# 提取倒数第二个200ms的数据（完全避开两端边界效应）
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# 窗口长度750，步长50 → start=750-100=650，end=750-50=700
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start_idx = self.window_size - 2 * self.step_size
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end_idx = self.window_size - self.step_size
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output_data = filtered[:, start_idx:end_idx].copy()
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return output_data
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def run(self):
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"""线程主逻辑：精确200ms触发一次滤波"""
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# 精确定时核心：基于perf_counter计算下一次执行时间，补偿sleep误差
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interval = self.step_sec # 200ms = 0.2秒
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next_run_time = time.perf_counter()
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while self.running.is_set():
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# 1. 等待到下一次执行时间（精确定时）
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current_time = time.perf_counter()
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if current_time < next_run_time:
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time.sleep(next_run_time - current_time)
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next_run_time += interval # 补偿：下次执行时间基于上一次目标时间
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else:
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# 若超时（如滤波耗时超过200ms），重置下一次时间（避免累积误差）
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algo_log("滤波耗时超过200ms，定时偏移", level='debug')
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next_run_time = time.perf_counter() + interval
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# 2. 执行滤波逻辑
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try:
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# 获取最新的3秒窗口数据
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window_data = self.ring_buffer.get_latest_n_points(self.window_size)
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algo_log(f"获取到{window_data.shape}数据", level='debug')
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if window_data is None:
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algo_log(f"缓存数据不足，当前缓存{self.ring_buffer.GetDataLenCount()}点，需{self.window_size}点", level='debug')
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continue
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# 滤波并提取无边界效应的200ms数据
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filtered_data = self._filter_window_data(window_data)
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algo_log(f"滤波后{filtered_data.shape}数据", level='debug')
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# 回调返回结果（外部可处理）
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if self.filter_result_callback is not None:
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self.filter_result_callback(filtered_data[:64, :]) # 只发送前64通道数据
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except Exception as e:
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algo_log(f"滤波执行异常: {e}", level='error')
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def set_result_callback(self, callback):
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"""注册滤波结果回调函数"""
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self.filter_result_callback = callback
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def stop(self):
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"""停止滤波线程（安全版）"""
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# 1. 先设置停止标志（Event.clear()是线程安全的）
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self.running.clear()
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# 2. 核心修复：只有线程已启动且正在运行时才调用join
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if self.is_alive():
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# 等待线程正常退出，最多1秒
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self.join(timeout=1)
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# 超时未退出时打印警告，便于排查问题
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if self.is_alive():
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algo_log("警告：滤波线程在1秒内未正常退出，可能存在阻塞操作", level="WARNING")
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# 3. 无论线程是否启动，都打印停止日志
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algo_log("滤波线程已停止")
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