""" datamock 验证脚本（模拟算法端） 作为 ZMQ ROUTER 监听 8100 端口，等待 datamock.py 连接并验证数据流 运行顺序: 第一步: python verify_datamock.py (先启动，监听 8100) 第二步: python datamock.py (后启动，连接 8100) """ import zmq import numpy as np import time import sys import matplotlib matplotlib.use('TkAgg') # 在导入 pyplot 之前确保 Tkinter 正确初始化 try: import tkinter as tk root = tk.Tk() root.withdraw() # 隐藏主窗口，我们只需要它的事件循环 except Exception as e: print(f"[WARN] Tkinter 初始化警告: {e}") import matplotlib.pyplot as plt from datetime import datetime # ===== 可视化参数 ===== PLOT_WINDOW_SEC = 2.0 # 滑动窗口时长（秒） PLOT_CHANNELS = [0, 1, 2, 3] # 要显示的 EEG 通道索引 SERVER_ADDR = 'tcp://127.0.0.1:8100' FS = 250 N_SAMPLES_PER_PKT = 5 N_CHAN = 66 EEG_FREQ = 10 EEG_AMP = 100.0 # EEG 幅值 100μV（峰值） EEG_AMP_MEAN = EEG_AMP * 2 / np.pi # 正弦波 |mean| ≈ 63.7μV EEG_AMP_TOLERANCE = 1.5 # 幅值容差倍数 LABEL_INTERVAL = 5 FFT_SAMPLES = 250 # 做一次 FFT 需要的采样点数（1s数据） EXPECTED_BYTES = N_SAMPLES_PER_PKT * N_CHAN * 4 # 1320 bytes (5*66*4) def validate_fft(samples): """对 Ch0 数据做 FFT，返回峰值频率""" freqs = np.fft.rfftfreq(FFT_SAMPLES, d=1 / FS) fft_mag = np.abs(np.fft.rfft(samples)) peak_idx = np.argmax(fft_mag[1:]) + 1 # 跳过 DC return freqs[peak_idx], fft_mag, freqs def main(): ctx = zmq.Context() sock = ctx.socket(zmq.ROUTER) sock.bind(SERVER_ADDR) print(f"[{datetime.now().strftime('%H:%M:%S')}] ZMQ ROUTER 绑定 {SERVER_ADDR}，等待 datamock.py 连接...\n") # ===== 初始化交互式绘图 ===== plt.ion() # 开启交互模式 fig = plt.figure(figsize=(14, 10)) fig.suptitle('EEG Data Monitor (Real-time)', fontsize=14) # 使用 GridSpec 进行布局 from matplotlib.gridspec import GridSpec gs = GridSpec(len(PLOT_CHANNELS) + 2, 1, figure=fig, hspace=0.3) axes = [] lines_eeg = [] for i, ch in enumerate(PLOT_CHANNELS): ax = fig.add_subplot(gs[i]) axes.append(ax) ax.set_ylabel(f'Ch{ch} (μV)', fontsize=8) ax.grid(True, alpha=0.3) ax.set_ylim(-150, 150) line, = ax.plot([], [], lw=0.8) lines_eeg.append(line) ax.set_title(f'EEG Channel {ch}', fontsize=9) # 标签通道子图 (Ch64 - 标签值) ax_label = fig.add_subplot(gs[len(PLOT_CHANNELS)]) axes.append(ax_label) ax_label.set_ylabel('Label Value', fontsize=8) ax_label.grid(True, alpha=0.3) ax_label.set_ylim(-0.5, 2.5) line_label, = ax_label.plot([], [], 'ro-', lw=1.5, markersize=4) line_label_data = line_label ax_label.set_title('Ch64 - Label Value', fontsize=9) # Ch65 标签序号子图 ax_seq = fig.add_subplot(gs[len(PLOT_CHANNELS) + 1]) axes.append(ax_seq) ax_seq.set_ylabel('Label Seq', fontsize=8) ax_seq.set_xlabel('Time (samples)', fontsize=8) ax_seq.grid(True, alpha=0.3) ax_seq.set_ylim(-0.5, 10) line_seq, = ax_seq.plot([], [], 'gs-', lw=1.5, markersize=4) line_seq_data = line_seq ax_seq.set_title('Ch65 - Label Sequence', fontsize=9) plt.tight_layout() # ===== 状态 ===== global_idx = 0 # 全局采样点索引 label_events = [] # 捕获的标签事件 start_time = None fft_done = False fft_buffer = [] # 暂存前 250 点做 FFT ch64_zero_ok = True # 验证 Ch64 非标签采样点均为 0 ch65_zero_ok = True # 验证 Ch65 非标签采样点均为 0 label_pos_ok_all = True # 验证标签均在包内索引 4 # ===== 数据缓冲区 ===== max_samples = int(FS * PLOT_WINDOW_SEC) eeg_buffer = {ch: np.zeros(max_samples) for ch in PLOT_CHANNELS} label_buffer = np.zeros(max_samples) seq_buffer = np.zeros(max_samples) time_axis = np.arange(max_samples) # ZMQ 收发统计 recv_count = 0 try: # 首次 pause 用于显示窗口 plt.pause(0.5) print(f"[INFO] 交互窗口已显示，如未看到请检查任务栏") while True: # ROUTER recv: prepended 一个 identity 帧 # datamock 发送 3帧 [b'datamock', b'', data_bytes] # ROUTER 接收后变成 4帧 [router_identity, b'datamock', b'', data_bytes] frames = sock.recv_multipart() recv_count += 1 now = time.time() if start_time is None: start_time = now # 帧格式: [router_identity, b'datamock', b'', data_bytes] router_id = frames[0] # ROUTER 添加的身份帧 identity = frames[1] # 发送端的 identity _empty = frames[2] # 空帧 raw_data = frames[3] # 实际数据字节 # 数据长度校验 if len(raw_data) != EXPECTED_BYTES: print(f"[ERROR] 数据长度错误: 期望{EXPECTED_BYTES}字节, 实际{len(raw_data)}字节") continue # 解析为 [5, 66] float32 数组 packet = np.frombuffer(raw_data, dtype=np.float32).reshape(N_SAMPLES_PER_PKT, N_CHAN) elapsed = now - start_time # ===== 验证 1: 数据形状 ===== if recv_count == 1: shape_ok = packet.shape == (N_SAMPLES_PER_PKT, N_CHAN) print(f"[{'✓' if shape_ok else '✗'}] 数据形状: {packet.shape} " f"(期望 [{N_SAMPLES_PER_PKT}, {N_CHAN}])") if not shape_ok: print(f" ✗ 形状不匹配，退出") break # ===== 验证 2: EEG 幅值（首包） ===== if recv_count == 1: eeg = packet[:, :64] amp_mean = np.mean(np.abs(eeg)) amp_ok = amp_mean <= EEG_AMP_MEAN * EEG_AMP_TOLERANCE print(f"[{'✓' if amp_ok else '✗'}] EEG 幅值: 均值={amp_mean:.2f}μV " f"(期望 ~{EEG_AMP_MEAN:.2f}μV，峰值 ~{EEG_AMP:.2f}μV)") if not amp_ok: print(f" ✗ 幅值超出容差范围") # ===== 验证 3: EEG 频率（首秒数据收集满后做 FFT） ===== fft_buffer.append(packet[:, 0].copy()) # 收集 Ch0 if not fft_done and len(fft_buffer) * N_SAMPLES_PER_PKT >= FFT_SAMPLES: # 凑够 250 点，做 FFT all_ch0 = np.concatenate(fft_buffer)[:FFT_SAMPLES] peak_freq, fft_mag, freqs = validate_fft(all_ch0) freq_ok = abs(peak_freq - EEG_FREQ) < 1.0 print(f"[{'✓' if freq_ok else '✗'}] EEG 频率: 峰值={peak_freq:.1f}Hz " f"(期望 ~{EEG_FREQ}Hz)") print(f" FFT 幅度谱前 5 峰值:") top5 = np.argsort(fft_mag[1:])[-5:][::-1] + 1 for rank, idx in enumerate(top5): print(f" {rank+1}. {freqs[idx]:.1f}Hz 幅度={fft_mag[idx]:.1f}") print() fft_done = True # ===== 验证 4: 标签通道（Ch64/Ch65） ===== ch64 = packet[:, 64] ch65 = packet[:, 65] ch64_nonzero = np.where(ch64 != 0)[0] ch65_nonzero = np.where(ch65 != 0)[0] # 检查非标签采样点是否全为 0 ch64_zeros = np.all(ch64[:4] == 0) ch65_zeros = np.all(ch65[:4] == 0) ch64_zero_ok = ch64_zero_ok and ch64_zeros ch65_zero_ok = ch65_zero_ok and ch65_zeros if len(ch64_nonzero) > 0: pos_in_pkt = int(ch64_nonzero[0]) label_val = int(ch64[pos_in_pkt]) label_seq = int(ch65[pos_in_pkt]) pos_ok = (len(ch64_nonzero) == 1 and pos_in_pkt == 4) label_pos_ok_all = label_pos_ok_all and pos_ok elapsed_since_start = now - start_time print(f"[✓] 标签触发 @ {elapsed_since_start:.1f}s " f"(global_idx={global_idx} 包{recv_count})") print(f" Ch64 标签值: {label_val} Ch65 序号: {label_seq}") print(f" 包内位置: 采样点 {pos_in_pkt}/4 " f"({'✓' if pos_ok else '✗ 期望 4'}) " f"其余采样点 Ch64=0: {'✓' if ch64_zeros else '✗'} " f"Ch65=0: {'✓' if ch65_zeros else '✗'}") print() label_events.append({ 'time': elapsed_since_start, 'label': label_val, 'seq': label_seq }) global_idx += N_SAMPLES_PER_PKT # ===== 更新绘图缓冲区 ===== for ch_idx, ch in enumerate(PLOT_CHANNELS): eeg_buffer[ch] = np.roll(eeg_buffer[ch], -N_SAMPLES_PER_PKT) eeg_buffer[ch][-N_SAMPLES_PER_PKT:] = packet[:, ch] label_buffer = np.roll(label_buffer, -N_SAMPLES_PER_PKT) label_buffer[-N_SAMPLES_PER_PKT:] = packet[:, 64] seq_buffer = np.roll(seq_buffer, -N_SAMPLES_PER_PKT) seq_buffer[-N_SAMPLES_PER_PKT:] = packet[:, 65] # ===== 实时更新绘图 ===== for i, ch in enumerate(PLOT_CHANNELS): lines_eeg[i].set_data(time_axis, eeg_buffer[ch]) # 数据已是 μV 单位 line_label_data.set_data(time_axis, label_buffer) line_seq_data.set_data(time_axis, seq_buffer) # 设置 x 轴范围 for ax in axes: ax.set_xlim(0, max_samples) # 刷新图形（交互模式） fig.canvas.draw_idle() plt.pause(0.001) except KeyboardInterrupt: print("\n" + "=" * 55) print(" 验证结果汇总") print("=" * 55) print(f" 运行时长: {time.time() - start_time:.1f}s") print(f" 收到包数: {recv_count}") print(f" FFT 验证: {'✓ 已完成' if fft_done else '✗ 未完成（时长不足1s）'}") print(f" 非标签采样点 Ch64=0: {'✓' if ch64_zero_ok else '✗'}") print(f" 非标签采样点 Ch65=0: {'✓' if ch65_zero_ok else '✗'}") print(f" 标签均在包内位置4: {'✓' if label_pos_ok_all else '✗'}") if label_events: print(f"\n 共捕获 {len(label_events)} 次标签事件:") for i, ev in enumerate(label_events): print(f" {i+1}. t={ev['time']:.1f}s label={ev['label']} 序号={ev['seq']}") # 标签间隔 print(f"\n 标签间隔验证 (期望 ~{LABEL_INTERVAL}s):") for i in range(1, len(label_events)): dt = label_events[i]['time'] - label_events[i-1]['time'] ok = abs(dt - LABEL_INTERVAL) < 0.1 print(f" {i}->{i+1}: {dt:.2f}s {'✓' if ok else '✗'}") # 标签交替 labels = [e['label'] for e in label_events] alt_ok = all(labels[i] != labels[i+1] for i in range(len(labels) - 1)) print(f"\n 标签交替: {labels} {'✓ 交替正确' if alt_ok else '✗ 交替错误'}") # 序号 label1_seqs = [e['seq'] for e in label_events if e['label'] == 1] label2_seqs = [e['seq'] for e in label_events if e['label'] == 2] s1_ok = label1_seqs == list(range(1, len(label1_seqs) + 1)) s2_ok = label2_seqs == list(range(1, len(label2_seqs) + 1)) print(f" label=1 序号: {label1_seqs} {'✓' if s1_ok else '✗'}") print(f" label=2 序号: {label2_seqs} {'✓' if s2_ok else '✗'}") else: print(f"\n 未捕获标签事件（运行时长不足 {LABEL_INTERVAL}s）") print("=" * 55) finally: sock.close() ctx.term() plt.ioff() plt.close('all') try: root.destroy() except: pass if __name__ == '__main__': main()