update log config

.gitignore

@@ -2,6 +2,7 @@
 __pycache__/
 
 # Distribution / packaging
+release/
 build/
 dist/
 dist_nuitka/

Decoder.py

@@ -62,6 +62,8 @@ class Decoder_main(threading.Thread):
 
         # 注册滤波结果回调（示例：打印数据形状）
         self.sliding_filter.filter_result_callback = self.zmqServer.send_filtered_data
+        # 注册 beta_psd 广播回调，每秒通过 8099 端口发送给上位机
+        self.sliding_filter.set_beta_broadcast_callback(lambda v: self.zmqServer.broadcast_message('beta_psd', v))
 
     def is_valid_signal(self, data, threshold=1e5):  # 判断当前信号是否为有效信号
         # data: (chans, samples)
@@ -155,8 +157,8 @@ class Decoder_main(threading.Thread):
         #     self.blink_b, self.blink_a = signal.butter(4, [self.l_freq / (self.device_info['sample_rate'] / 2), self.h_freq / (self.device_info['sample_rate'] / 2)], btype='band')
 
     def parameter_init(self,bandPass_low,bandPass_high):
-        self.interval_epoch = [int(i * self.device_info['sample_rate']) for i in self.interval_epoch]  # epoch截取信息
-        self.train_epoch = [int(self.interval_epoch[0]), int(self.interval_epoch[1] + 0.1 * self.device_info['sample_rate'])]  # 训练样本epoch
+        self.interval_epoch = [int(i * self.device_info['sample_rate']) for i in self.interval_epoch]  # epoch截取信息 ssmvep [50, 550]
+        self.train_epoch = [int(self.interval_epoch[0]), int(self.interval_epoch[1] + 0.1 * self.device_info['sample_rate'])]  # 训练样本epoch ssmevep [50, 575]
         self.trainData = [] #训练数据
         self.trainLabel = [] #训练标签
         self.plotData = [] #报告分析数据
@@ -204,6 +206,9 @@ class Decoder_main(threading.Thread):
                 self.zmqServer.state_mode = 'rest'
 
             try:
+                if self.zmqServer.open_Impedance:
+                    time.sleep(0.005)
+                    continue
                 if self.decoder_class == 'ssvep' or self.decoder_class == 'pvs':
                     self.decoder_SSVEP()
                 elif self.decoder_class == 'ssmvep':
@@ -213,7 +218,7 @@ class Decoder_main(threading.Thread):
                 else:
                     if self.zmqServer.paradigmBuffer.GetDataLenCount() < 25:
                         time.sleep(0.005)
-                        continue;
+                        continue
                     self.zmqServer.paradigmBuffer.getData(25)
             except Exception as e:
                 algo_log(f"Decoder Loop Error: {e}")
@@ -231,7 +236,7 @@ class Decoder_main(threading.Thread):
         if self.zmqServer.open_Impedance:  # 阻抗检测状态不解码
             return
         data = self.zmqServer.paradigmBuffer.getDataViaSSVEP(50)
-        # algo_log(f"SSVEP取出的：{data.shape}, data = {data[:20]}", level="DEBUG")
+        # algo_log(f"SSVEP取出的：{data.shape}, data = {data[:, :10]}", level="DEBUG")
         data = data[:self.n_chan, :]
         if self.decodingSteps == 1 and hasattr(self,'dw'):  # 开始预热
             self.dw.onlineInit()  # 刺激闪烁的第1s重置 --在线数据采集时
@@ -284,6 +289,7 @@ class Decoder_main(threading.Thread):
                         and self.trainLabel.count(self.currentLabel) < self.single_train:
                     self.trainData.append(trainTrial)
                     self.trainLabel.append(self.currentLabel)
+                    algo_log(f"SSMVEP训练集：{np.shape(self.trainData)}", level="DEBUG")
             else:
                 time.sleep(0.0001)
                 return
@@ -423,7 +429,7 @@ class Decoder_main(threading.Thread):
                 y_pred = torch.max(Cls, 1)[1]
                 self.plotLabel.append(int(y_pred.item()))
                 algo_log(f"MI运动意图识别: {y_pred}")
-            self.zmqServer.broadcast_message('paradigm', int(y_pred.item()))
+            self.zmqServer.broadcast_message('result', int(y_pred.item()))
             end = time.time()
             algo_log(f'MI发送给界面完成,耗时{end - start:.3f}s。')
         else:  # 休息状态

MI/Algorithm/conformer_2class.py

@@ -318,11 +318,7 @@ class ExP():
                 train_pred = torch.max(outputs, 1)[1]
                 train_acc = float((train_pred == label).cpu().numpy().astype(int).sum()) / float(label.size(0))
 
-                algo_log('Epoch:', e,
-                      '  Train loss: %.6f' % loss.detach().cpu().numpy(),
-                      '  Test loss: %.6f' % loss_test.detach().cpu().numpy(),
-                      '  Train accuracy %.6f' % train_acc,
-                      '  Test accuracy is %.6f' % acc, level="debug")
+                algo_log(f"Epoch = {e}, Train loss = {loss.detach().cpu().numpy():.6f}, Test loss = {loss_test.detach().cpu().numpy():.6f}, Train accuracy = {train_acc:.6f}, Test accuracy = {acc:.6f}", level="debug")
 
                 self.log_write.write(str(e) + "    " + str(acc) + "\n")
                 num = num + 1
@@ -335,8 +331,8 @@ class ExP():
 
         torch.save(self.model, model_path)
         averAcc = averAcc / num
-        algo_log('The average accuracy is:', averAcc, level="debug")
-        algo_log('The best accuracy is:', bestAcc, level="debug")
+        algo_log(f"The average accuracy is: {averAcc}", level="debug")
+        algo_log(f"The best accuracy is: {bestAcc}", level="debug")
         self.log_write.write('The average accuracy is: ' + str(averAcc) + "\n")
         self.log_write.write('The best accuracy is: ' + str(bestAcc) + "\n")
 
@@ -366,12 +362,13 @@ def onlineTrain(data_queue,result_queue):
         data = data_queue.get(timeout=30)
         all_data, all_label,model_path,n_chan = data['data'], data['label'],data['modelPath'],data['n_chan']
         exp = ExP(n_chan)
-        algo_log('训练参数： ',np.shape(all_data),np.shape(all_label),model_path, level="debug")
+        algo_log(f"训练参数： {np.shape(all_data)}, {np.shape(all_label)}, {model_path}", level="debug")
         bestAcc, averAcc, Y_true, Y_pred = exp.train(all_data,all_label,model_path)
-        algo_log('THE BEST ACCURACY IS ' + str(bestAcc), level="debug")
+        algo_log(f"THE BEST ACCURACY IS {str(bestAcc)}", level="debug")
 
         endtime = datetime.datetime.now()
-        algo_log('train duration: ',str(endtime - starttime), level="debug")
+        algo_log(f"train duration: {endtime - starttime}", level="debug")
+        
 
         # 将模型或参数传回
         result_queue.put({
@@ -387,7 +384,7 @@ def offlineTrain(all_data,all_label,modelPath):
 
     # seed_n = np.random.randint(2025)
     seed_n = 1877
-    algo_log('seed is ' + str(seed_n), level="debug")
+    algo_log(f"seed is {seed_n}", level="debug")
     random.seed(seed_n)
     np.random.seed(seed_n)
     torch.manual_seed(seed_n)
@@ -400,7 +397,7 @@ def offlineTrain(all_data,all_label,modelPath):
     algo_log('THE BEST ACCURACY IS ' + str(bestAcc), level="debug")
 
     endtime = datetime.datetime.now()
-    algo_log('train duration: ',str(endtime - starttime), level="debug")
+    algo_log(f"train duration: {endtime - starttime}", level="debug")
 
 
 

README.md

@@ -13,6 +13,13 @@ Debug_64ch_Decoder_Optimize is an updated version that fixes several issues and
 6. decoder class切换问题
 7. decoder_class切换时，数据重置、各类参数重置
 
+# realease log
+- 2026年6月11日11:29:17 打包第一版，包名runDecoder.dist_v0.0.0_beta_20260611.7z
+- 2026年6月11日12:00:00 打包第二版，包名runDecoder.dist_v0.0.0_beta_20260611.7z
+    - 修复上位机先发decoder_class, 后发open_impedence 带来decoder_main thread 阻塞问题
+
+- 2026年6月12日15:05:47 runDecoder.dist_v0.0.2_beta_20260612
+    - 优化filter读数精度
 
 #  常用命令
 source activate 3in1Py310
@@ -22,7 +29,15 @@ python ZeroMQClient_mock.py
 python filter_test.py
 python upperHost_stimmock/MI_headless.py
 
+# 打包命令
+./nuitka_3in1_package.sh
 
-
-# 遗留问题
+# TODO
 1. mvep是否要把list freq 开放到config
+2. 滤波器参数 放到config文件
+
+# debug log
+## MI
+Epoch采集完成|收到命令: {'method': 'train'|取出的
+
+收到命令: {'method': 'train'|收到命令: {'method': 'train'|收到命令: {'method': 'predict'|事件检测到

SSVEP/dwfbcca.py

@@ -19,10 +19,10 @@ class FbccaDw:
     def __init__(self, fs, num_target, num_chans, num_filter, num_harms, stimTime, parameter, width, winNum,method):
         algo_log('******************************************', level="debug")
         algo_log('parameter list',level="debug")
-        algo_log('target:', num_target, level="debug")
-        algo_log('number of filter bank:', num_filter, level="debug")
-        algo_log('parameter:', parameter, level="debug")
-        algo_log('width:', width, level="debug")
+        algo_log(f"target: {num_target}", level="debug")
+        algo_log(f"number of filter bank: {num_filter}", level="debug")
+        algo_log(f"parameter: {parameter}", level="debug")
+        algo_log(f"width: {width}", level="debug")
         self.phase = 0
         self.bandWidth = width
         self.winNum = winNum

Tools/beta_calculate.py

@@ -20,7 +20,7 @@ class Beta_Calculate():
         alpha_psd = np.sum(self.band_psd(freqs, psd, (8, 13)))
         theta_psd = np.sum(self.band_psd(freqs, psd, (4, 8)))
         
-        print(f"[功率] β={beta_psd:.2f} | α={alpha_psd:.2f} | θ={theta_psd:.2f}")
+        # print(f"[功率] β={beta_psd:.2f} | α={alpha_psd:.2f} | θ={theta_psd:.2f}")
 
         return beta_psd, alpha_psd, theta_psd
 

Zmq/filterProcess.py

@@ -5,8 +5,13 @@
 import numpy as np
 import time
 import threading
+import queue
 from scipy import signal
 from logs.log import algo_log
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from Tools.beta_calculate import Beta_Calculate
 
 class FilterRingBuffer:
     def __init__(self, n_chan, n_points):
@@ -89,7 +94,74 @@ class FilterRingBuffer:
             self.has_new_data = False  # 重置时清空新数据标记
 
 # -----------------------------------------------------------------------------
-# 2. 独立滑动滤波类（仅负责滤波业务逻辑，不关心缓存实现）
+# 2. 独立 Beta PSD 计算线程（避免阻塞滤波主循环的 200ms 定时）
+# -----------------------------------------------------------------------------
+class BetaPsdCalculator(threading.Thread):
+    """独立的 Beta PSD 计算线程，使用队列与滤波主线程解耦"""
+
+    def __init__(self, fs=250, window_size=750):
+        super().__init__(daemon=True)
+        self.fs = fs
+        self.window_size = window_size
+        self._beta_calc = Beta_Calculate(Threshold_value_low=0, Threshold_value_high=0, fs=fs)
+        self._input_queue = queue.Queue(maxsize=2)
+        self._running = threading.Event()
+        self._running.set()
+        self._latest_beta = None
+        self._beta_lock = threading.Lock()
+        self.beta_broadcast_callback = None
+
+    def push_data(self, data):
+        """供外部调用的线程安全数据推送接口"""
+        try:
+            self._input_queue.put_nowait(data)
+        except queue.Full:
+            try:
+                self._input_queue.get_nowait()
+            except queue.Empty:
+                pass
+            try:
+                self._input_queue.put_nowait(data)
+            except queue.Full:
+                pass
+
+    def get_latest_beta(self):
+        """获取最新的 beta 值（线程安全）"""
+        with self._beta_lock:
+            return self._latest_beta
+
+    def run(self):
+        while self._running.is_set():
+            try:
+                data = self._input_queue.get(timeout=1.5)
+                if data is None:
+                    break
+                try:
+                    beta_psd, _, _ = self._beta_calc.calculate_all(
+                        data, fs=self.fs, nperseg=min(self.window_size, data.shape[1])
+                    )
+                    with self._beta_lock:
+                        self._latest_beta = round(float(beta_psd), 3)
+                    if self.beta_broadcast_callback is not None:
+                        self.beta_broadcast_callback(self._latest_beta)
+                except Exception as e:
+                    algo_log(f"Beta PSD 计算异常: {e}", level='error')
+            except queue.Empty:
+                pass
+
+    def stop(self):
+        """停止计算线程"""
+        self._running.clear()
+        try:
+            self._input_queue.put_nowait(None)
+        except queue.Full:
+            pass
+        if self.is_alive():
+            self.join(timeout=2)
+
+
+# -----------------------------------------------------------------------------
+# 3. 独立滑动滤波类（仅负责滤波业务逻辑，不关心缓存实现）
 # -----------------------------------------------------------------------------
 class SlidingFilter(threading.Thread):
     def __init__(
@@ -118,14 +190,32 @@ class SlidingFilter(threading.Thread):
         # 滤波结果回调（外部可注册，获取滤波后的数据）
         self.filter_result_callback = None
 
+        # beta 每秒触发计数（200ms步长，5次 = 1s）
+        self._beta_step_counter = 0
+        self._beta_steps_per_second = max(1, int(round(1.0 / step_sec)))  # 5
+
+        self.slide_window = None  # 滑动窗口缓存 (n_chan, window_size)
+        self.slide_ready = False  # 窗口是否已填满初始数据
         # 预计算滤波器系数（仅执行一次）
         self._init_filters()
 
+        # 独立的 Beta 计算线程（避免阻塞滤波主循环）
+        self._beta_thread = BetaPsdCalculator(fs=srate, window_size=self.window_size)
+
+    def start(self):
+        """同时启动 Beta 计算线程和滤波主线程"""
+        self._beta_thread.start()
+        super().start()
+
+    def set_beta_broadcast_callback(self, callback):
+        """注册 Beta PSD 广播回调函数"""
+        self._beta_thread.beta_broadcast_callback = callback
+
     def _init_filters(self):
         """预计算所有滤波器系数（仅执行一次）"""
         # 50Hz工频陷波（Q=30，工业标准）
         self.b_notch, self.a_notch = signal.iirnotch(50, 30, self.srate)
-        # 8~30Hz带通FIR（65阶，线性相位）
+        # 0.5~45Hz带通FIR（65阶，线性相位）
         self.b_bp = signal.firwin(
             numtaps=65,
             cutoff=[0.5/(self.srate/2), 45/(self.srate/2)],
@@ -135,7 +225,7 @@ class SlidingFilter(threading.Thread):
         self.a_bp = np.array([1.0])
 
     def _filter_window_data(self, window_data):
-        """对3秒窗口数据执行滤波，返回无边界效应的200ms数据"""
+        """对3秒窗口数据执行滤波，返回 (无边界效应的200ms数据, 完整3s滤波数据)"""
         # 零相位滤波（无延迟，无边界效应）
         filtered = window_data - np.mean(window_data, axis=-1, keepdims=True)
         filtered = signal.filtfilt(self.b_notch, self.a_notch, filtered, axis=-1)
@@ -146,39 +236,64 @@ class SlidingFilter(threading.Thread):
         start_idx = self.window_size - 2 * self.step_size
         end_idx = self.window_size - self.step_size
         output_data = filtered[:, start_idx:end_idx].copy()
-        return output_data
+        return output_data, filtered
 
     def run(self):
         """线程主逻辑：精确200ms触发一次滤波"""
-        interval = self.step_sec  # 200ms = 0.2秒
-        next_run_time = time.perf_counter()
-        while self.running.is_set():
-            # 1. 精确定时等待
-            current_time = time.perf_counter()
-            if current_time < next_run_time:
-                time.sleep(next_run_time - current_time)
-                next_run_time += interval
-            else:
-                algo_log("滤波耗时超过200ms，定时偏移", level='debug')
-                next_run_time = time.perf_counter() + interval
+        interval = self.step_sec  # 0.2s
+        # 以启动时刻为绝对时间基准（核心改动）
+        base_time = time.perf_counter()
+        frame_count = 0  # 帧计数器，用于对齐时序
 
-            # ========== 新增核心判断：无新数据则直接跳过 ==========
+        while self.running.is_set():
+            # 计算理论执行时刻：严格按帧序号 × 步长
+            expect_time = base_time + frame_count * interval
+            current_time = time.perf_counter()
+
+            # 精确定时等待
+            if current_time < expect_time:
+                time.sleep(expect_time - current_time)
+            else:
+                # 处理超时：仅告警，不重置基准（防止累积偏移）
+                algo_log(f"滤波任务超时，偏移 {(current_time - expect_time)*1000:.1f} ms", level='debug')
+            
+            frame_count += 1  # 帧序号自增，保证周期绝对稳定
             if not self.ring_buffer.check_and_clear_new_data():
                 # 无新数据，不执行滤波、不发送数据
                 continue
             
-            # 2. 有新数据，才执行原有滤波逻辑
+            # ========== 原有滤波逻辑 ==========
             try:
-                window_data = self.ring_buffer.get_latest_n_points(self.window_size)
-                if window_data is None:
-                    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')
+                if not self.slide_ready:
+                    # 阶段1：首次填满3s初始窗口
+                    full_data = self.ring_buffer.get_latest_n_points(self.window_size)
+                    if full_data is None:
+                        algo_log("初始窗口数据不足", level='debug')
                         continue
+                    self.slide_window = full_data
+                    self.slide_ready = True
+                else:
+                    # 阶段2：正常滑动 → 取最新50个新点，增量拼接
+                    new_step_data = self.ring_buffer.get_latest_n_points(self.step_size)
+                    if new_step_data is None:
+                        algo_log("滑动步长数据不足", level='debug')
+                        continue
+                    # 增量滑动：丢弃前50点，拼接新50点（标准滑动窗口）
+                    self.slide_window = np.hstack([
+                        self.slide_window[:, self.step_size:],
+                        new_step_data
+                    ])
                 
-                filtered_data = self._filter_window_data(window_data)
-                # algo_log(f"滤波后{filtered_data.shape}数据", level='debug')
+                filtered_data, filtered_full = self._filter_window_data(self.slide_window[:64, :])
+
+                # Beta PSD 每秒计算一次
+                self._beta_step_counter += 1
+                if self._beta_step_counter >= self._beta_steps_per_second:
+                    self._beta_step_counter = 0
+                    self._beta_thread.push_data(filtered_full[:2, :])
 
                 if self.filter_result_callback is not None:
-                    self.filter_result_callback(filtered_data[:64, :])
+                    self.filter_result_callback(filtered_data)
             except Exception as e:
                 algo_log(f"滤波执行异常: {e}", level='error')
 
@@ -187,17 +302,11 @@ class SlidingFilter(threading.Thread):
         self.filter_result_callback = callback
 
     def stop(self):
-        """停止滤波线程（安全版）"""
-        # 1. 先设置停止标志（Event.clear()是线程安全的）
+        """停止滤波线程和 Beta 计算线程"""
+        self._beta_thread.stop()
         self.running.clear()
-        
-        # 2. 核心修复：只有线程已启动且正在运行时才调用join
         if self.is_alive():
-            # 等待线程正常退出，最多1秒
             self.join(timeout=1)
-            # 超时未退出时打印警告，便于排查问题
             if self.is_alive():
                 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")
-        
-        # 3. 无论线程是否启动，都打印停止日志
         algo_log("滤波线程已停止")

Zmq/zmqServer.py

@@ -152,6 +152,7 @@ class zmqServer(threading.Thread):
                 msg = {'method': method, 'params': params}
                 msg_bytes = json.dumps(msg).encode('utf-8')
 
+                if msg['method'] != 'beta_psd':
                     algo_log(f"发送命令结果: {msg}", level="DEBUG")
                 
                 # 广播到所有命令客户端
@@ -179,7 +180,7 @@ class zmqServer(threading.Thread):
         # 转置为上位机需要的[50, 通道数]格式
         filtered_data = filtered_data.T.astype(np.float64)
         send_buf = filtered_data.tobytes()
-        algo_log(f"发送滤波数据，长度: {len(send_buf)}字节, filtered_data.shape: {filtered_data.shape}", level="DEBUG", record_once=True)
+        # algo_log(f"发送滤波数据，长度: {len(send_buf)}字节, filtered_data.shape: {filtered_data.shape}", level="DEBUG", record_once=True)
         self.data_send_queue.put(send_buf)
 
     def _process_data_send_queue(self):
@@ -225,6 +226,9 @@ class zmqServer(threading.Thread):
             algo_log(f"无效JSON命令: {message_bytes.hex()}", level="ERROR")
             self.broadcast_message("error", {"code": 400, "message": "无效JSON格式"})
             return
+        except Exception as e:
+            algo_log(f"_handle_cmd_message exception: {e}", level="ERROR")
+            return
             
         algo_log(f"收到命令: {message}", level="INFO")
         method = message.get("method")
@@ -270,6 +274,22 @@ class zmqServer(threading.Thread):
             elif params == 2: #停止解码
                 self.IsExitApp = True
                 self.running = False
+            
+            resp = {
+                "method": "predict_response",
+                "params": {
+                    "code": 200,
+                    "message": "ok"
+                }
+            }
+            try:
+                resp_bytes = json.dumps(resp, ensure_ascii=False).encode("utf-8")
+                self.cmd_socket.send_multipart([ident, b"", resp_bytes])
+                algo_log(f"predict 命令已即时回复客户端 {ident}", level="DEBUG")
+            except Exception as e:
+                algo_log(f"predict 命令回复失败: {e}", level="ERROR")
+            return
+            
         elif method == "rest":
             self.state_mode = 'rest'
         elif method == "impedance":
@@ -353,8 +373,8 @@ class zmqServer(threading.Thread):
     def detect_event(self, samples):
         self.pack_contain_event = False
         # 第65通道为事件通道
-        event = int(samples[-2][0])
-        # for idx, event in enumerate(events):
+        events = np.array(samples[-2], dtype=np.int32).tolist()
+        for idx, event in enumerate(events):
             if event in self.events:
                 new_key = "".join(
                     [
@@ -368,8 +388,8 @@ class zmqServer(threading.Thread):
                     self.count_events[new_key] = self.latency + 1
                 else:
                     self.count_events[new_key] = self.train_latency + 1
-            self.event_inner_idx = self.device_info['frame_points'] - 1
-            # algo_log(f"事件检测到: {event}，索引: {idx}", level="DEBUG")
+                self.event_inner_idx = idx
+                algo_log(f"事件检测到: {events}，索引: {idx}", level="DEBUG")
                 self.pack_contain_event = True   
                 
         # 倒计时并清理过期事件
@@ -405,13 +425,18 @@ class zmqServer(threading.Thread):
                     frames = self.cmd_socket.recv_multipart()
                     self._handle_cmd_message(frames)
 
-                # 处理8100数据端口消息
+                # 处理8100数据端口消息（排空积压，消除标签延迟）
                 if self.data_socket in socks and socks[self.data_socket] == zmq.POLLIN:
-                    frames = self.data_socket.recv_multipart()
+                    while True:
+                        try:
+                            frames = self.data_socket.recv_multipart(zmq.NOBLOCK)
                             self._handle_data_message(frames)
+                        except zmq.Again:
+                            break
 
         except Exception as e:
-            algo_log(f"服务器主循环异常: {e}", level="ERROR")
+            algo_log(f"服务器主循环异常: {str(e)}", level="ERROR")
+            return
         finally:
             self.running = False
             # 优雅关闭所有资源

config.ini

@@ -18,11 +18,23 @@ Upper_Port = 8088
 Decoder_Host = 127.0.0.1
 Decoder_Port = 8099
 Serial_port = COM44
-algo_log_level = DEBUG
-console_output = 1
 save_train_data = 0
 zmqServer_host = 127.0.0.1
 
+[algo_log]
+# ========== 文件日志配置 ==========
+file_log_enable = true
+file_log_level = DEBUG
+log_path = exe
+retention_days = 3
+
+# ========== 控制台/黑框配置 ==========
+console_enable = true
+console_show_window = true
+console_log_level = DEBUG
+
+
+
 ; 64 导设备配置
 [device_type_1]
 sample_rate = 250

filter_test.py

@@ -56,7 +56,7 @@ EMPTY_FRAME = b""
 
 # 仿真信号配置
 TARGET_CHANNEL = 0
-SIGNAL_FREQ_LIST = [3, 13]
+SIGNAL_FREQ_LIST = [13]
 SIGNAL_AMP = 1.8
 NOISE_GAUSSIAN_AMP = 0.4
 NOISE_POWER50_AMP = 0.3
@@ -128,8 +128,8 @@ def generate_eeg_packet(pkt_idx: int) -> np.ndarray:
         sig = 0.0
         for freq in SIGNAL_FREQ_LIST:
             sig += SIGNAL_AMP * np.sin(2 * np.pi * freq * t_arr)
-        sig += NOISE_POWER50_AMP * np.sin(2 * np.pi * 50 * t_arr)
-        sig += NOISE_GAUSSIAN_AMP * np.random.randn(n_point)
+        # sig += NOISE_POWER50_AMP * np.sin(2 * np.pi * 50 * t_arr)
+        # sig += NOISE_GAUSSIAN_AMP * np.random.randn(n_point)
         data[:, ch] = sig
 
     # 事件通道、保留通道

logs/log.py

@@ -1,114 +1,172 @@
 import os
+import sys
+from pathlib import Path
 from datetime import datetime, timedelta
 import logging
 from logging.handlers import RotatingFileHandler
 import inspect
+try:
+    import win32gui
+    import win32con
+    WIN32_AVAILABLE = True
+except ImportError:
+    WIN32_AVAILABLE = False
+
 from PubLibrary.InifileHelper import IniRead
 
-# 全局配置
-console_output = IniRead('system', 'console_output', '1')
-log_level = IniRead('system', 'algo_log_level', 'INFO')
+# ===================== 新增：获取 EXE 同级目录 =====================
+def get_app_root():
+    """获取 runDecoder.exe 所在的真实根目录（兼容 onefile / standalone）"""
+    if getattr(sys, 'frozen', False):
+        # Nuitka / PyInstaller 打包后走这里
+        app_path = sys.executable
+    else:
+        # 本地源码运行时，取当前脚本目录
+        app_path = os.path.abspath(__file__)
+    return os.path.dirname(app_path)
+
+# 程序根目录（exe 同级）
+APP_ROOT = Path(get_app_root())
+# 日志文件夹名：exe 同级下 logs 目录
+DEFAULT_LOG_DIR = APP_ROOT / "logs"
+
+# ===================== 读取 [algo_log] 配置 =====================
+# 文件日志
+FILE_LOG_ENABLE = IniRead("algo_log", "file_log_enable", "true").lower() == "true"
+FILE_LOG_LEVEL = IniRead("algo_log", "file_log_level", "DEBUG").upper()
+# 优先级：配置文件 > 默认exe同级logs
+CFG_LOG_PATH = IniRead("algo_log", "log_path", "").strip()
+if CFG_LOG_PATH == "exe":
+    LOG_DIR = DEFAULT_LOG_DIR
+else:
+    LOG_DIR = Path(CFG_LOG_PATH)
+
+LOG_RETENTION_DAYS = int(IniRead("algo_log", "retention_days", 3))
+
+# 控制台日志 + 黑框控制
+CONSOLE_ENABLE = IniRead("algo_log", "console_enable", "true").lower() == "true"
+CONSOLE_SHOW_WINDOW = IniRead("algo_log", "console_show_window", "true").lower() == "true"
+CONSOLE_LOG_LEVEL = IniRead("algo_log", "console_log_level", "INFO").upper()
+
+# ===================== 全局常量与缓存 =====================
 log_once_cache = set()
 logger_cache = {}
-LOG_RETENTION_DAYS = 3
-LOG_DIR = './logs/'
 LOG_FILE_PREFIX = 'algo_log_'
+# 确保日志目录存在
+LOG_DIR.mkdir(parents=True, exist_ok=True)
+LOG_DIR_STR = str(LOG_DIR) + "\\"
 
-# 日志格式：时间 - 日志器名 - 级别 - 文件名:行号 - 函数名 - 日志内容
+# 日志格式
 LOG_FORMAT = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
 DATE_FORMAT = '%Y-%m-%d %H:%M:%S'
 
+# 日志级别映射
+LEVEL_MAP = {
+    "DEBUG": logging.DEBUG,
+    "INFO": logging.INFO,
+    "WARNING": logging.WARNING,
+    "ERROR": logging.ERROR,
+    "FATAL": logging.FATAL
+}
+FILE_LOG_LEVEL_INT = LEVEL_MAP.get(FILE_LOG_LEVEL, logging.INFO)
+CONSOLE_LOG_LEVEL_INT = LEVEL_MAP.get(CONSOLE_LOG_LEVEL, logging.INFO)
 
-def clean_old_logs():
-    """清理超过指定天数的旧日志文件"""
+# ===================== Windows 控制台黑框显示/隐藏 =====================
+def control_console_window():
+    if not sys.platform.startswith("win") or not WIN32_AVAILABLE:
+        return
     try:
-        if not os.path.exists(LOG_DIR):
+        hwnd = win32gui.GetForegroundWindow()
+        if CONSOLE_SHOW_WINDOW:
+            win32gui.ShowWindow(hwnd, win32con.SW_SHOW)
+        else:
+            win32gui.ShowWindow(hwnd, win32con.SW_HIDE)
+    except Exception:
+        pass
+
+control_console_window()
+
+# ===================== 清理过期日志 =====================
+def clean_old_logs():
+    try:
+        if not LOG_DIR.exists():
             return
         expire_date = datetime.now() - timedelta(days=LOG_RETENTION_DAYS)
         for filename in os.listdir(LOG_DIR):
-            if not filename.startswith(LOG_FILE_PREFIX) or not filename.endswith('.log'):
+            if not (filename.startswith(LOG_FILE_PREFIX) and filename.endswith('.log')):
                 continue
             date_str = filename[len(LOG_FILE_PREFIX):-4]
             try:
                 file_date = datetime.strptime(date_str, '%Y-%m-%d')
                 if file_date < expire_date:
-                    file_path = os.path.join(LOG_DIR, filename)
+                    file_path = LOG_DIR / filename
                     os.remove(file_path)
-                    print(f"清理过期日志: {file_path}")
             except ValueError:
                 continue
-    except Exception as e:
-        print(f"清理旧日志异常: {str(e)}")
-
+    except Exception:
+        pass
 
+# ===================== 初始化日志器 =====================
 def init_module_logger(logger_name):
-    """初始化日志器 + 清理旧日志"""
-    os.makedirs(LOG_DIR, exist_ok=True)
-    clean_old_logs()
-
-    current_date = datetime.now().strftime("%Y-%m-%d")
-    log_file = os.path.join(LOG_DIR, f"{LOG_FILE_PREFIX}{current_date}.log")
-
     if logger_name in logger_cache:
         return logger_cache[logger_name]
 
+    clean_old_logs()
+
     logger = logging.getLogger(logger_name)
-    logger.setLevel(log_level)
+    logger.setLevel(logging.DEBUG)
     if logger.handlers:
         logger_cache[logger_name] = logger
         return logger
 
-    # 文件输出处理器
+    formatter = logging.Formatter(LOG_FORMAT, datefmt=DATE_FORMAT)
+
+    # 文件日志
+    if FILE_LOG_ENABLE:
+        current_date = datetime.now().strftime("%Y-%m-%d")
+        log_file = LOG_DIR / f"{LOG_FILE_PREFIX}{current_date}.log"
         file_handler = RotatingFileHandler(
             log_file,
             maxBytes=10 * 1024 * 1024,
             backupCount=10,
             encoding='utf-8'
         )
-    formatter = logging.Formatter(LOG_FORMAT, datefmt=DATE_FORMAT)
         file_handler.setFormatter(formatter)
+        file_handler.setLevel(FILE_LOG_LEVEL_INT)
         logger.addHandler(file_handler)
 
-    # 控制台输出
-    if console_output:
-        console_handler = logging.StreamHandler()
+    # 控制台日志
+    if CONSOLE_ENABLE:
+        console_handler = logging.StreamHandler(sys.stdout)
         console_handler.setFormatter(formatter)
+        console_handler.setLevel(CONSOLE_LOG_LEVEL_INT)
         logger.addHandler(console_handler)
 
     logger_cache[logger_name] = logger
     return logger
 
-
+# ===================== 对外日志入口函数 =====================
 def algo_log(content, level="INFO", record_once=False):
-    """
-    日志入口函数
-    自动记录：调用文件名、代码行号、所在函数
-    """
-    # 回溯栈帧，获取真正调用 algo_log 的代码位置
-    # f_back(1) -> algo_log 自身，f_back(2) -> 业务调用处
-    frame = inspect.currentframe().f_back.f_back
-    if not frame:
-        file_name = "unknown"
-    else:
-        file_name = os.path.basename(frame.f_code.co_filename)
+    frame = inspect.currentframe()
+    if frame:
+        frame = frame.f_back.f_back
+    file_name = os.path.basename(frame.f_code.co_filename) if frame else "unknown"
 
     logger = init_module_logger(file_name)
 
-    # 单次日志去重
     if record_once:
         log_key = f"{level.upper()}_{content}"
         if log_key in log_once_cache:
             return
         log_once_cache.add(log_key)
 
-    # 日志级别分发
     level_upper = level.upper()
-    log_map = {
+    log_func_map = {
         "DEBUG": logger.debug,
+        "INFO": logger.info,
         "WARNING": logger.warning,
         "ERROR": logger.error,
-        "FATAL": logger.fatal,
-        "INFO": logger.info
+        "FATAL": logger.fatal
     }
-    log_func = log_map.get(level_upper, logger.info)
+    log_func = log_func_map.get(level_upper, logger.info)
     log_func(content)

nuitka_3in1_package.sh

@@ -28,7 +28,6 @@ echo "输出目录：${OUT_DIR}"
 python -m nuitka \
 --standalone \
 --msvc=latest \
---windows-console-mode=disable \
 --module-parameter=torch-disable-jit=yes \
 --enable-plugin=no-qt \
 --include-package=numpy \

runDecoder.py

@@ -1,7 +1,7 @@
-import matplotlib
-matplotlib.use('Agg')
-import argparse
-import sys
+# import matplotlib
+# matplotlib.use('Agg')
+# import argparse
+# import sys
 import time
 from Decoder import Decoder_main
 from PubLibrary.RunOnce import is_program_running

upperHost_stimmock/MI_headless.py

@@ -170,6 +170,7 @@ def run_headless():
 
     time.sleep(1)  # 等待连接建立
     client.send_data('decoderClass', 'mi')
+    time.sleep(4)  # 等待 zmqServer 排空启动积压包（datamock 提前连接会积压 ~3s 数据）
 
     # MI_IntervalEpoch = [0.5, 4.5]，trial时长 = 4.5-0.5 = 4.0s
     _mi_iv = ast.literal_eval(IniRead('system', 'MI_IntervalEpoch'))  # [0.5, 4.5]
@@ -222,7 +223,7 @@ def run_headless():
                 time.sleep(0.5)  # ding 提示后等待
 
                 client.send_data('train', 0)
-                time.sleep(train_time + epoch_wait)  # 等待刺激时间 + epoch 完成时间
+                time.sleep(train_time + 0.2)  # 等待刺激时间 + epoch 完成时间
 
                 trained += 1
                 client.send_data('rest', 0)
@@ -231,7 +232,7 @@ def run_headless():
                 # 空闲态样本采集（train 1，label=2）
                 print(f"\n[Train] 空闲态采集 (train 1) trained={trained}")
                 client.send_data('train', 1)
-                time.sleep(train_time + epoch_wait)  # 等待刺激时间 + epoch 完成时间
+                time.sleep(train_time + 0.2)   # 等待刺激时间 + epoch 完成时间
 
                 trained += 1
                 client.send_data('rest', 0)