351 lines
12 KiB
Python
351 lines
12 KiB
Python
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# -*- coding: utf-8 -*-
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"""
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脑电滤波服务 8100端口测试工具【最终修复版】
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修复：1. Matplotlib中文字体乱码 2. ZMQ双连接收不到数据问题
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通信规范：
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上位机 -> 服务端：send_multipart([client_id, b"", data_buf]) 共3帧
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服务端 recv_multipart() 帧长度 = 3
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时序：每20ms(0.02s)发送一包 (5,66)，服务端200ms回传 (50,64)
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"""
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import sys
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import time
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import threading
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import logging
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import traceback
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from collections import deque
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import numpy as np
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import zmq
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import matplotlib.pyplot as plt
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from matplotlib.animation import FuncAnimation
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# ===================== 全局前置：修复Matplotlib中文字体 & 负号显示 =====================
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plt.rcParams["font.sans-serif"] = ["SimHei", "Microsoft YaHei", "WenQuanYi Micro Hei"]
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plt.rcParams["axes.unicode_minus"] = False # 解决负号显示异常
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# ===================== 【1. 全局可配置参数区】 =====================
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# ZMQ 服务端配置
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ZMQ_SERVER_IP = "127.0.0.1"
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ZMQ_SERVER_PORT = 8100
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ZMQ_SOCKET_TIMEOUT = 3000 # 套接字超时(ms)
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POLL_TIMEOUT = 10 # Poll轮询超时(ms)，不影响发包时序
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# 数据报文配置（严格对齐业务）
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PKG_SEND_SHAPE = (5, 66) # 发送包 shape (点数, 总通道)
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PKG_RECV_SHAPE = (50, 64) # 滤波回包 shape (点数, 脑电通道)
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SEND_INTERVAL = 0.02 # 上位机发包间隔 20ms
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SAMPLE_RATE = 250 # 采样率 Hz
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# 通道定义
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CH_EEG = 64
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CH_EVENT = 64
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CH_RESERVED = 65
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# ZMQ 三帧报文固定字段（和你服务端代码完全一致）
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CLIENT_ID = b"test_client_001"
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EMPTY_FRAME = b""
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# 仿真信号配置（可自由调参测试滤波）
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TARGET_CHANNEL = 0
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SIGNAL_FREQ_LIST = [10.0, 22.0]
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SIGNAL_AMP = 1.8
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NOISE_GAUSSIAN_AMP = 0.4
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NOISE_POWER50_AMP = 0.3
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EVENT_LABEL_VAL = 1
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RESERVED_VAL = 0.0
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# 可视化配置
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MAX_PLOT_POINTS = 800
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PLOT_REFRESH_INTERVAL = 80
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FFT_N_POINTS = 256
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PLOT_X_LIMIT_FREQ = (0, 60)
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# 运行控制
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MAX_RUN_SECONDS = None
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ENABLE_RECONNECT = True
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PRINT_STAT_INTERVAL = 5.0
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# ===================== 【2. 全局变量 & 线程安全】 =====================
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g_running = threading.Event()
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g_running.set()
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data_lock = threading.Lock()
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# 绘图数据缓冲区
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raw_data_buf = deque(maxlen=MAX_PLOT_POINTS)
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filt_data_buf = deque(maxlen=MAX_PLOT_POINTS)
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# 运行统计
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stat = {
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"send_cnt": 0,
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"recv_cnt": 0,
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"start_time": time.perf_counter(),
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"last_print_time": time.perf_counter()
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}
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# ===================== 【3. 日志配置】 =====================
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def init_logger():
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log_format = "%(asctime)s | %(levelname)-8s | %(message)s"
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logging.basicConfig(
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level=logging.INFO,
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format=log_format,
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datefmt="%Y-%m-%d %H:%M:%S"
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)
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return logging.getLogger("FilterTest")
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logger = init_logger()
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# ===================== 【4. 仿真脑电数据生成 (5,66)】 =====================
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def generate_eeg_packet(pkt_idx: int) -> np.ndarray:
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"""生成单包 (5,66) 仿真数据：脑电+噪声+工频+事件通道+保留通道"""
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n_point, n_chan = PKG_SEND_SHAPE
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base_t = pkt_idx * n_point / SAMPLE_RATE
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t_arr = base_t + np.arange(n_point) / SAMPLE_RATE
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data = np.zeros((n_point, n_chan), dtype=np.float64)
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# 64路脑电：多频信号 + 50Hz工频 + 高斯白噪声
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for ch in range(CH_EEG):
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sig = 0.0
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for freq in SIGNAL_FREQ_LIST:
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sig += SIGNAL_AMP * np.sin(2 * np.pi * freq * t_arr)
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sig += NOISE_POWER50_AMP * np.sin(2 * np.pi * 50 * t_arr)
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sig += NOISE_GAUSSIAN_AMP * np.random.randn(n_point)
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data[:, ch] = sig
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# 事件通道、保留通道赋值
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data[:, CH_EVENT] = EVENT_LABEL_VAL
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data[:, CH_RESERVED] = RESERVED_VAL
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return data
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# ===================== 【5. 核心修复：单DEALER连接 + Poller 同时收发】 =====================
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def zmq_io_thread():
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"""
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唯一ZMQ工作线程：单个DEALER连接，同时发包+收包（对齐真实上位机）
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使用 Poller 多路复用，避免阻塞、超时报错
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"""
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context = zmq.Context()
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pkt_index = 0
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send_interval = SEND_INTERVAL
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while g_running.is_set():
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try:
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# 新建 DEALER 套接字（全局唯一连接）
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sock = context.socket(zmq.DEALER)
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sock.setsockopt(zmq.RCVTIMEO, ZMQ_SOCKET_TIMEOUT)
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sock.setsockopt(zmq.SNDTIMEO, ZMQ_SOCKET_TIMEOUT)
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sock.connect(f"tcp://{ZMQ_SERVER_IP}:{ZMQ_SERVER_PORT}")
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logger.info(f"ZMQ 连接成功 -> {ZMQ_SERVER_IP}:{ZMQ_SERVER_PORT}")
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# 注册Poller：监听当前套接字的可读事件
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poller = zmq.Poller()
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poller.register(sock, zmq.POLLIN)
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# 精准发包计时（消除sleep漂移）
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next_send_ts = time.perf_counter()
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while g_running.is_set():
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# 1. 运行时长限制判断
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if MAX_RUN_SECONDS is not None:
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run_sec = time.perf_counter() - stat["start_time"]
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if run_sec > MAX_RUN_SECONDS:
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logger.info(f"已到达设定运行时长 {MAX_RUN_SECONDS}s，停止任务")
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return
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# 2. Poll 轮询：有数据就接收，无数据继续执行发包逻辑
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socks_ready = dict(poller.poll(POLL_TIMEOUT))
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if sock in socks_ready:
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# ========== 接收服务端回包 (multipart) ==========
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frames = sock.recv_multipart()
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if not frames:
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continue
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# 取最后一帧为有效滤波数据
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recv_bytes = frames[-1]
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if not recv_bytes:
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continue
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# 解析为 (50,64) float64
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filt_data = np.frombuffer(recv_bytes, dtype=np.float64)
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expect_size = PKG_RECV_SHAPE[0] * PKG_RECV_SHAPE[1]
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if filt_data.size != expect_size:
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logger.warning(f"回包长度异常：实际{filt_data.size}，预期{expect_size}")
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continue
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filt_data = filt_data.reshape(PKG_RECV_SHAPE)
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# 统计 + 写入绘图缓冲区
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stat["recv_cnt"] += 1
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with data_lock:
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filt_data_buf.extend(filt_data[:, TARGET_CHANNEL])
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# 定时打印运行状态
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now = time.perf_counter()
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if now - stat["last_print_time"] > PRINT_STAT_INTERVAL:
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run_sec = now - stat["start_time"]
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loss_rate = (stat["send_cnt"] - stat["recv_cnt"]) / stat["send_cnt"] * 100 if stat["send_cnt"] > 0 else 0.0
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logger.info(
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f"运行:{run_sec:.1f}s | 发包:{stat['send_cnt']} | 收包:{stat['recv_cnt']} | 丢包率:{loss_rate:.2f}%"
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)
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stat["last_print_time"] = now
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# 3. 精准定时发包（严格20ms间隔）
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current_ts = time.perf_counter()
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if current_ts >= next_send_ts:
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# 生成 (5,66) 仿真数据包
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pkt_data = generate_eeg_packet(pkt_index)
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pkt_index += 1
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send_buf = pkt_data.tobytes()
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# ========== 三帧Multipart发送（和你服务端代码完全一致） ==========
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sock.send_multipart([CLIENT_ID, EMPTY_FRAME, send_buf])
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# 统计 + 写入原始数据缓冲区
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stat["send_cnt"] += 1
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with data_lock:
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raw_data_buf.extend(pkt_data[:, TARGET_CHANNEL])
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# 更新下一次发包时间戳
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next_send_ts += send_interval
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except zmq.ZMQError as e:
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# 区分正常超时 和 网络异常
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if e.errno == zmq.EAGAIN:
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continue
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logger.warning(f"ZMQ 连接异常: {e}")
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sock.close()
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poller.unregister(sock)
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if not ENABLE_RECONNECT:
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break
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logger.info("500ms 后尝试重连...")
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time.sleep(0.5)
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except Exception as e:
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logger.error(f"IO线程未知异常:\n{traceback.format_exc()}")
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break
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context.term()
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logger.info("ZMQ IO 线程已退出")
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# ===================== 【6. 可视化绘图（无逻辑改动，已前置修复字体）】 =====================
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def init_plot():
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fig = plt.figure(figsize=(14, 9))
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fig.suptitle(f"脑电滤波测试 | 观测通道: {TARGET_CHANNEL}", fontsize=14)
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ax1 = plt.subplot(2, 2, 1)
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ax1.set_title("原始输入波形 (含噪声+工频)")
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ax1.set_ylabel("幅值")
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ax1.grid(True, alpha=0.3)
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line_raw, = ax1.plot([], [], color="#1f77b4", linewidth=1)
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ax2 = plt.subplot(2, 2, 2)
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ax2.set_title("滤波后输出波形")
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ax2.set_ylabel("幅值")
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ax2.grid(True, alpha=0.3)
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line_filt, = ax2.plot([], [], color="#d62728", linewidth=1)
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ax3 = plt.subplot(2, 2, 3)
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ax3.set_title("原始信号频谱")
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ax3.set_xlabel("频率 (Hz)")
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ax3.set_xlim(*PLOT_X_LIMIT_FREQ)
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ax3.grid(True, alpha=0.3)
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line_raw_fft, = ax3.plot([], [], color="#1f77b4")
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ax4 = plt.subplot(2, 2, 4)
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ax4.set_title("滤波后信号频谱")
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ax4.set_xlabel("频率 (Hz)")
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ax4.set_xlim(*PLOT_X_LIMIT_FREQ)
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ax4.grid(True, alpha=0.3)
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line_filt_fft, = ax4.plot([], [], color="#d62728")
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plt.tight_layout(rect=[0, 0, 1, 0.96])
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return fig, [line_raw, line_filt, line_raw_fft, line_filt_fft], [ax1, ax2, ax3, ax4]
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def update_plot(frame, lines, axes):
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line_raw, line_filt, line_raw_fft, line_filt_fft = lines
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ax1, ax2, ax3, ax4 = axes
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with data_lock:
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raw_data = list(raw_data_buf)
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filt_data = list(filt_data_buf)
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# 时域波形
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if raw_data:
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x_raw = np.arange(len(raw_data))
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line_raw.set_data(x_raw, raw_data)
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ax1.relim()
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ax1.autoscale_view()
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if filt_data:
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x_filt = np.arange(len(filt_data))
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line_filt.set_data(x_filt, filt_data)
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ax2.relim()
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ax2.autoscale_view()
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# 频谱计算（汉宁窗减少频谱泄露）
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def calc_fft(sig, n_fft):
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if len(sig) < n_fft:
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return [], []
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win = np.hanning(n_fft)
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sig_win = sig[-n_fft:] * win
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fft_vals = np.fft.fft(sig_win)
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fft_amp = np.abs(fft_vals)[:n_fft//2]
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freq = np.fft.fftfreq(n_fft, 1/SAMPLE_RATE)[:n_fft//2]
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return freq, fft_amp
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freq_raw, amp_raw = calc_fft(raw_data, FFT_N_POINTS)
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freq_filt, amp_filt = calc_fft(filt_data, FFT_N_POINTS)
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line_raw_fft.set_data(freq_raw, amp_raw)
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line_filt_fft.set_data(freq_filt, amp_filt)
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ax3.relim()
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ax3.autoscale_view(scaley=True)
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ax4.relim()
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ax4.autoscale_view(scaley=True)
|
|||
|
|
|
|||
|
|
return lines
|
|||
|
|
|
|||
|
|
# ===================== 【7. 资源释放 & 主入口】 =====================
|
|||
|
|
def clean_resource():
|
|||
|
|
g_running.clear()
|
|||
|
|
logger.info("开始停止所有线程...")
|
|||
|
|
time.sleep(0.3)
|
|||
|
|
plt.close("all")
|
|||
|
|
logger.info("资源释放完成")
|
|||
|
|
|
|||
|
|
def main():
|
|||
|
|
logger.info("=" * 60)
|
|||
|
|
logger.info("脑电滤波测试客户端 【修复版】启动")
|
|||
|
|
logger.info(f"服务端地址: {ZMQ_SERVER_IP}:{ZMQ_SERVER_PORT}")
|
|||
|
|
logger.info(f"发包格式: {PKG_SEND_SHAPE} | 间隔: {SEND_INTERVAL*1000:.0f}ms")
|
|||
|
|
logger.info(f"回包格式: {PKG_RECV_SHAPE} | ZMQ三帧报文 [客户端ID, 空帧, 数据帧]")
|
|||
|
|
logger.info("=" * 60)
|
|||
|
|
|
|||
|
|
# 启动唯一ZMQ收发线程
|
|||
|
|
io_thread = threading.Thread(target=zmq_io_thread, daemon=True, name="ZMQ_IO_Thread")
|
|||
|
|
io_thread.start()
|
|||
|
|
|
|||
|
|
# 启动可视化绘图
|
|||
|
|
fig, lines, axes = init_plot()
|
|||
|
|
ani = FuncAnimation(
|
|||
|
|
fig, update_plot,
|
|||
|
|
fargs=(lines, axes),
|
|||
|
|
interval=PLOT_REFRESH_INTERVAL,
|
|||
|
|
blit=True,
|
|||
|
|
cache_frame_data=False
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 主线程阻塞，监听关闭
|
|||
|
|
try:
|
|||
|
|
plt.show()
|
|||
|
|
except KeyboardInterrupt:
|
|||
|
|
logger.info("收到 Ctrl+C 中断信号，准备退出")
|
|||
|
|
finally:
|
|||
|
|
# 输出最终统计
|
|||
|
|
run_total = time.perf_counter() - stat["start_time"]
|
|||
|
|
loss_rate = (stat["send_cnt"] - stat["recv_cnt"]) / stat["send_cnt"] * 100 if stat["send_cnt"] > 0 else 0.0
|
|||
|
|
logger.info(f"\n===== 运行汇总 =====")
|
|||
|
|
logger.info(f"总运行时长: {run_total:.1f} s")
|
|||
|
|
logger.info(f"总发包数: {stat['send_cnt']}")
|
|||
|
|
logger.info(f"总收包数: {stat['recv_cnt']}")
|
|||
|
|
logger.info(f"整体丢包率: {loss_rate:.2f} %")
|
|||
|
|
clean_resource()
|
|||
|
|
sys.exit(0)
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
main()
|