422 lines
14 KiB
Python
422 lines
14 KiB
Python
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# -*- coding: utf-8 -*-
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"""
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ZMQ 脑电数据测试工具【语法错误修复版】
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修复点：
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1. dataclass 可变列表默认值报错
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2. threading.Thread daemon 参数语法错误
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适配：Python3.10、全链路 float64、ZMQ DEALER<->ROUTER
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端口：8099(命令) / 8100(数据)
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"""
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import zmq
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import time
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import threading
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import numpy as np
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import matplotlib.pyplot as plt
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import json
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from dataclasses import dataclass, field
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from typing import Dict, List, Optional, Union, Tuple
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from matplotlib.animation import FuncAnimation
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# ===================== 1. 配置管理 =====================
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@dataclass(frozen=True) # 冻结配置类
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class TestConfig:
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# 网络配置
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SERVER_IP: str = "127.0.0.1"
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CMD_PORT: int = 8099
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DATA_PORT: int = 8100
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# 硬件与时序
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SAMPLE_RATE: int = 250
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FRAME_INTERVAL_MS: int = 20
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SEND_INTERVAL: float = FRAME_INTERVAL_MS / 1000
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CHANNEL_NUMS: int = 66
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FRAME_POINTS: int = 5
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FILTER_OUT_CHAN: int = 64
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FILTER_FRAME_POINTS: int = 50
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# 数据类型 & 字节数 (float64 8字节)
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DATA_DTYPE: np.dtype = np.float64
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RAW_FRAME_BYTES: int = CHANNEL_NUMS * FRAME_POINTS * 8 # 66*5*8 = 2640
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FILTER_FRAME_BYTES: int = FILTER_OUT_CHAN * FILTER_FRAME_POINTS * 8 # 25600
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# 事件通道索引
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EVENT_CHANNEL_IDX: int = -2
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# 列表类型 使用 default_factory 规避可变默认值报错
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EVENT_TAGS: List[int] = field(default_factory=lambda: [1, 2, 99])
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SIM_SIGNAL_FREQ: List[float] = field(default_factory=lambda: [8.0, 9.0])
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# 仿真噪声
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NOISE_STD: float = 0.25
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# 可视化配置
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PLOT_TARGET_CHAN: int = 0
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PLOT_WINDOW_LEN: int = 400
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PLOT_REFRESH_INTERVAL: int = 50
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# 日志限流
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FRAME_ERR_INTERVAL: float = 3.0
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# ZMQ 配置
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SEND_RETRY_MAX: int = 3
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SEND_RETRY_SLEEP: float = 0.01
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ZMQ_HWM: int = 1000
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# 初始化全局配置
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CONFIG = TestConfig()
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# ===================== 2. 全局状态管理 =====================
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class GlobalState:
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def __init__(self):
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self.run_flag: bool = True
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self.last_frame_err_time: float = 0.0
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GLOBAL_STATE = GlobalState()
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# ===================== 3. Matplotlib 中文初始化 =====================
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def init_matplotlib():
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# Windows 黑体，Linux/Mac 自行替换字体
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plt.rcParams['font.sans-serif'] = ['SimHei']
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plt.rcParams['axes.unicode_minus'] = False # 修复负号乱码
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init_matplotlib()
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# ===================== 4. ZMQ DEALER 客户端 =====================
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class ZmqDealerClient:
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"""适配 ROUTER 的 DEALER 客户端，高频流式数据专用"""
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def __init__(self, server_ip: str, port: int):
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self.ctx: zmq.Context = zmq.Context()
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self.socket: zmq.Socket = self.ctx.socket(zmq.DEALER)
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self._configure_socket()
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self.socket.connect(f"tcp://{server_ip}:{port}")
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def _configure_socket(self):
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"""套接字参数配置"""
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self.socket.setsockopt(zmq.RCVHWM, CONFIG.ZMQ_HWM)
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self.socket.setsockopt(zmq.SNDHWM, CONFIG.ZMQ_HWM)
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self.socket.setsockopt(zmq.RCVTIMEO, 0)
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self.socket.setsockopt(zmq.SNDTIMEO, 0)
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def send_json(self, data: Dict) -> bool:
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"""发送JSON命令，带重试机制"""
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try:
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payload = json.dumps(data, ensure_ascii=False).encode("utf-8")
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except Exception as e:
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print(f"[JSON序列化失败] {e}")
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return False
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for _ in range(CONFIG.SEND_RETRY_MAX):
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try:
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self.socket.send_multipart([b"", payload])
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return True
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except zmq.Again:
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time.sleep(CONFIG.SEND_RETRY_SLEEP)
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except Exception as e:
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print(f"[JSON发送异常] {e}")
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time.sleep(CONFIG.SEND_RETRY_SLEEP)
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print(f"[JSON发送重试失败]")
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return False
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def send_bytes(self, data: bytes) -> bool:
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"""发送二进制脑电数据，带重试"""
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for _ in range(CONFIG.SEND_RETRY_MAX):
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try:
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self.socket.send_multipart([b"", data])
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return True
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except zmq.Again:
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time.sleep(CONFIG.SEND_RETRY_SLEEP)
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except Exception as e:
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print(f"[二进制发送异常] {e}")
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time.sleep(CONFIG.SEND_RETRY_SLEEP)
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print(f"[二进制发送重试失败]")
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return False
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def recv_json(self) -> Optional[Dict]:
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"""接收JSON命令响应（标准3帧）"""
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try:
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frames = self.socket.recv_multipart()
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if len(frames) < 3:
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self._log_frame_err(f"帧数异常: {len(frames)}")
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return None
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payload = frames[2].decode("utf-8")
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return json.loads(payload)
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except json.JSONDecodeError:
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self._log_frame_err("JSON解析失败")
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return None
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except Exception as e:
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self._log_frame_err(f"接收异常: {e}")
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return None
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def recv_bytes(self) -> Optional[bytes]:
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"""接收滤波数据，兼容3/4帧格式"""
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try:
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frames = self.socket.recv_multipart()
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frame_len = len(frames)
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if frame_len == 3:
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payload = frames[2]
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elif frame_len == 4:
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payload = frames[3]
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else:
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self._log_frame_err(f"帧数异常: {frame_len}")
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return None
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if len(payload) != CONFIG.FILTER_FRAME_BYTES:
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self._log_frame_err(f"字节不匹配: 期望{CONFIG.FILTER_FRAME_BYTES}, 实际{len(payload)}")
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return None
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return payload
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except Exception as e:
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self._log_frame_err(f"数据接收异常: {e}")
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return None
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def _log_frame_err(self, msg: str):
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"""日志限流，防止刷屏"""
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now = time.time()
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if now - GLOBAL_STATE.last_frame_err_time > CONFIG.FRAME_ERR_INTERVAL:
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print(f"[帧异常] {msg}")
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GLOBAL_STATE.last_frame_err_time = now
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def close(self):
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"""优雅释放ZMQ资源"""
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try:
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self.socket.close(linger=0)
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self.ctx.term()
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except Exception as e:
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print(f"[资源释放异常] {e}")
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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self.close()
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# ===================== 5. 仿真脑电数据生成 =====================
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def generate_raw_eeg_frame(add_event: bool = False) -> np.ndarray:
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"""生成单帧float64仿真脑电数据"""
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t = np.linspace(
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0, CONFIG.FRAME_POINTS / CONFIG.SAMPLE_RATE,
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CONFIG.FRAME_POINTS, endpoint=False
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)
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eeg_frame = np.zeros(
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(CONFIG.CHANNEL_NUMS, CONFIG.FRAME_POINTS),
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dtype=CONFIG.DATA_DTYPE
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)
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# 模拟脑电信号 + 高斯噪声
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for freq in CONFIG.SIM_SIGNAL_FREQ:
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eeg_frame[:CONFIG.FILTER_OUT_CHAN] += np.sin(2 * np.pi * freq * t)
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eeg_frame[:CONFIG.FILTER_OUT_CHAN] += np.random.normal(
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0, CONFIG.NOISE_STD,
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size=(CONFIG.FILTER_OUT_CHAN, CONFIG.FRAME_POINTS)
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)
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# 事件通道处理
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eeg_frame[CONFIG.EVENT_CHANNEL_IDX] = 0.0
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if add_event:
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event_pos = np.random.randint(0, CONFIG.FRAME_POINTS)
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eeg_frame[CONFIG.EVENT_CHANNEL_IDX, event_pos] = np.random.choice(CONFIG.EVENT_TAGS)
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# 预留通道置0
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eeg_frame[-1] = 0.0
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return eeg_frame
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# ===================== 6. 后台工作线程 =====================
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def start_cmd_response_thread(cmd_client: ZmqDealerClient):
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"""命令响应接收线程"""
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print("[线程-命令接收] 已启动")
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while GLOBAL_STATE.run_flag:
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msg = cmd_client.recv_json()
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if msg:
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print(f"\n【命令响应】{json.dumps(msg, ensure_ascii=False, indent=2)}")
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time.sleep(0.01)
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print("[线程-命令接收] 已退出")
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def start_raw_eeg_send_thread(data_client: ZmqDealerClient):
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"""原始脑电发送线程（20ms/帧）"""
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print(f"[线程-原始数据发送] 20ms/帧 | 单帧{CONFIG.RAW_FRAME_BYTES}字节 | float64")
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frame_count = 0
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while GLOBAL_STATE.run_flag:
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insert_event = (frame_count % 20 == 0)
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eeg_frame = generate_raw_eeg_frame(add_event=insert_event)
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frame_bytes = eeg_frame.tobytes()
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# 字节校验
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if len(frame_bytes) != CONFIG.RAW_FRAME_BYTES:
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print(f"[字节警告] 期望{CONFIG.RAW_FRAME_BYTES}, 实际{len(frame_bytes)}")
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time.sleep(CONFIG.SEND_INTERVAL)
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frame_count += 1
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continue
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data_client.send_bytes(frame_bytes)
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frame_count += 1
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time.sleep(CONFIG.SEND_INTERVAL)
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print("[线程-原始数据发送] 已退出")
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def start_filter_data_recv_thread(data_client: ZmqDealerClient, plot_queue: List[np.ndarray]):
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"""滤波数据接收线程"""
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print(f"[线程-滤波数据接收] 单包{CONFIG.FILTER_FRAME_BYTES}字节 | float64")
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while GLOBAL_STATE.run_flag:
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raw_bytes = data_client.recv_bytes()
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if not raw_bytes:
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time.sleep(0.01)
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continue
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try:
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filter_arr = np.frombuffer(raw_bytes, dtype=CONFIG.DATA_DTYPE)
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filter_arr = filter_arr.reshape(CONFIG.FILTER_FRAME_POINTS, CONFIG.FILTER_OUT_CHAN)
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plot_queue.append(filter_arr[:, CONFIG.PLOT_TARGET_CHAN])
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except Exception as e:
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print(f"[滤波数据解析异常] {e}")
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continue
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print("[线程-滤波数据接收] 已退出")
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# ===================== 7. 实时波形可视化 =====================
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def start_wave_visualization(plot_queue: List[np.ndarray]):
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"""启动实时滤波波形绘图"""
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fig, ax = plt.subplots(figsize=(14, 4))
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x_axis = np.arange(0, CONFIG.PLOT_WINDOW_LEN)
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wave_data = np.zeros(CONFIG.PLOT_WINDOW_LEN, dtype=CONFIG.DATA_DTYPE)
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line, = ax.plot(x_axis, wave_data, color="#2E86AB", linewidth=1.2)
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ax.set_title(
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f"实时滤波脑电波形 | 通道 {CONFIG.PLOT_TARGET_CHAN} | {CONFIG.SAMPLE_RATE}Hz | float64",
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fontsize=12
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)
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ax.set_ylim(-3.0, 3.0)
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ax.grid(True, alpha=0.3, linestyle="--")
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plt.tight_layout()
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def update_plot(_):
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nonlocal wave_data
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if plot_queue:
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new_wave = plot_queue.pop(0)
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wave_data = np.roll(wave_data, -len(new_wave))
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wave_data[-len(new_wave)] = new_wave
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line.set_ydata(wave_data)
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return (line,)
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ani = FuncAnimation(
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fig, update_plot,
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interval=CONFIG.PLOT_REFRESH_INTERVAL,
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blit=True,
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cache_frame_data=False
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)
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plt.show()
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# ===================== 8. 全量业务测试用例 =====================
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def run_full_test_cases(cmd_client: ZmqDealerClient):
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"""全覆盖 zmqServer 所有命令：sync/targetFreqs/decoderClass/impedance/train/predict/rest"""
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print("\n" + "="*60)
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print("开始执行全量命令测试用例")
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print("="*60)
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time.sleep(2)
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# 1. 同步命令
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print("\n[用例 1] 发送 sync 命令")
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cmd_client.send_json({"method": "sync", "params": {}})
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time.sleep(1)
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|
|
|
|||
|
|
# 2. 设置目标频率
|
|||
|
|
print("\n[用例 2] 发送 targetFreqs = [8.0, 9.0]")
|
|||
|
|
cmd_client.send_json({"method": "targetFreqs", "params": [8.0, 9.0]})
|
|||
|
|
time.sleep(1)
|
|||
|
|
|
|||
|
|
# 3. 切换解码器
|
|||
|
|
print("\n[用例 3] 切换解码器为 ssmvep")
|
|||
|
|
cmd_client.send_json({"method": "decoderClass", "params": "ssmvep"})
|
|||
|
|
time.sleep(2)
|
|||
|
|
print("\n[用例 3-2] 切换解码器为 mi")
|
|||
|
|
cmd_client.send_json({"method": "decoderClass", "params": "mi"})
|
|||
|
|
time.sleep(2)
|
|||
|
|
|
|||
|
|
# 4. 阻抗检测开关
|
|||
|
|
print("\n[用例 4] 开启阻抗检测 impedance=1")
|
|||
|
|
cmd_client.send_json({"method": "impedance", "params": 1})
|
|||
|
|
time.sleep(1)
|
|||
|
|
print("\n[用例 4-2] 关闭阻抗检测 impedance=2")
|
|||
|
|
cmd_client.send_json({"method": "impedance", "params": 2})
|
|||
|
|
time.sleep(1)
|
|||
|
|
|
|||
|
|
# 5. 训练模式
|
|||
|
|
print("\n[用例 5] 启动训练 train，标签=1")
|
|||
|
|
cmd_client.send_json({"method": "train", "params": 1})
|
|||
|
|
time.sleep(3)
|
|||
|
|
|
|||
|
|
# # 6. 休息模式
|
|||
|
|
# print("\n[用例 6] 切换 rest 休息模式")
|
|||
|
|
# cmd_client.send_json({"method": "rest", "params": {}})
|
|||
|
|
# time.sleep(1)
|
|||
|
|
|
|||
|
|
# 7. 启动解码
|
|||
|
|
print("\n[用例 7] 启动解码 predict=1")
|
|||
|
|
cmd_client.send_json({"method": "predict", "params": 1})
|
|||
|
|
time.sleep(4)
|
|||
|
|
|
|||
|
|
# # 8. 非法命令（异常测试）
|
|||
|
|
# print("\n[用例 8] 发送非法命令 test_cmd_illegal")
|
|||
|
|
# cmd_client.send_json({"method": "test_cmd_illegal", "params": {}})
|
|||
|
|
# time.sleep(1)
|
|||
|
|
|
|||
|
|
# # 9. 停止解码
|
|||
|
|
# print("\n[用例 9] 停止解码 predict=2")
|
|||
|
|
# cmd_client.send_json({"method": "predict", "params": 2})
|
|||
|
|
# time.sleep(2)
|
|||
|
|
|
|||
|
|
print("\n" + "="*60)
|
|||
|
|
print("所有测试用例执行完毕")
|
|||
|
|
print("="*60)
|
|||
|
|
|
|||
|
|
# ===================== 主程序入口（修复线程语法） =====================
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
print("="*60)
|
|||
|
|
print("ZMQ 脑电仿真测试工具 启动")
|
|||
|
|
print(f"命令端口: {CONFIG.CMD_PORT} | 数据端口: {CONFIG.DATA_PORT}")
|
|||
|
|
print(f"原始帧{CONFIG.RAW_FRAME_BYTES}字节 | 滤波帧{CONFIG.FILTER_FRAME_BYTES}字节 | float64")
|
|||
|
|
print("="*60)
|
|||
|
|
|
|||
|
|
try:
|
|||
|
|
with ZmqDealerClient(CONFIG.SERVER_IP, CONFIG.CMD_PORT) as cmd_client, \
|
|||
|
|
ZmqDealerClient(CONFIG.SERVER_IP, CONFIG.DATA_PORT) as data_client:
|
|||
|
|
|
|||
|
|
plot_queue = []
|
|||
|
|
|
|||
|
|
# ========== 重点修复：线程语法，daemon 移出 args ==========
|
|||
|
|
# 命令接收线程
|
|||
|
|
t_cmd = threading.Thread(
|
|||
|
|
target=start_cmd_response_thread,
|
|||
|
|
args=(cmd_client,), # 单元素元组保留逗号
|
|||
|
|
daemon=True
|
|||
|
|
)
|
|||
|
|
# 原始数据发送线程
|
|||
|
|
t_eeg = threading.Thread(
|
|||
|
|
target=start_raw_eeg_send_thread,
|
|||
|
|
args=(data_client,),
|
|||
|
|
daemon=True
|
|||
|
|
)
|
|||
|
|
# 滤波数据接收线程
|
|||
|
|
t_filter = threading.Thread(
|
|||
|
|
target=start_filter_data_recv_thread,
|
|||
|
|
args=(data_client, plot_queue),
|
|||
|
|
daemon=True
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 启动线程
|
|||
|
|
t_cmd.start()
|
|||
|
|
t_eeg.start()
|
|||
|
|
t_filter.start()
|
|||
|
|
|
|||
|
|
# 执行测试用例
|
|||
|
|
run_full_test_cases(cmd_client)
|
|||
|
|
|
|||
|
|
# 启动可视化（阻塞主线程）
|
|||
|
|
print("\n[提示] 波形窗口已启动，关闭窗口 / Ctrl+C 退出程序")
|
|||
|
|
start_wave_visualization(plot_queue)
|
|||
|
|
|
|||
|
|
except KeyboardInterrupt:
|
|||
|
|
print("\n\n[用户中断] 接收到 Ctrl+C，准备退出...")
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"\n[程序异常] {e}")
|
|||
|
|
finally:
|
|||
|
|
# 停止所有后台线程
|
|||
|
|
GLOBAL_STATE.run_flag = False
|
|||
|
|
time.sleep(0.2)
|
|||
|
|
print("程序已安全退出")
|