"""
MI_headless.py
无界面版 MI 运动想象范式通讯流程模拟脚本。
复现 MI_main.py 的完整指令序列（train 0/1, rest, predict, saveData），
但不依赖 psychopy 也不打开任何窗口/音频，用 time.sleep 替代帧循环等待。

启动顺序:
    1. runDecoder.py
    2. datamock.py
    3. MI_headless.py
"""

import sys
import os
import json
import time
import threading
import zmq
import numpy as np
import ast
from datetime import datetime

sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from PubLibrary.InifileHelper import IniRead

personname = 'demo'
session = '01'

DATAMOCK_LABEL_ADDR = 'tcp://127.0.0.1:8101'  # datamock 标签命令地址


# ========== ZMQ 结果接收服务 ==========
class ZmqResultServer(threading.Thread):
    def __init__(self, port=8088):
        threading.Thread.__init__(self)
        self.port = port
        self.running = True
        self.energy = 0
        self.paradigm = 0   # 0=个体校准, 1=康复训练, 2=等待模型训练
        self.ChoosenNum = -1
        self.context = zmq.Context()
        self.socket = self.context.socket(zmq.ROUTER)
        self.socket.bind(f"tcp://0.0.0.0:{self.port}")
        self.daemon = True
        self.trial_idx = 0

    def run(self):
        print(f"[Server] UpperHost_Server listening on {self.port}")
        while self.running:
            try:
                frames = self.socket.recv_multipart(zmq.NOBLOCK)
                if len(frames) < 3:
                    continue
                message = json.loads(frames[2].decode('utf-8'))
                method = message.get('method')
                params = message.get('params')
                if method == 'energy':
                    self.energy = params
                elif method == 'paradigm':
                    self.paradigm = params
                    print(f"[Server] paradigm -> {params}")
                elif method == 'result':
                    self.ChoosenNum = params
                    self.trial_idx += 1
                    print(f"[Server] result={self.ChoosenNum}  (trial {self.trial_idx})")
            except zmq.Again:
                time.sleep(0.005)
            except Exception as e:
                print(f"[Server] error: {e}")

    def stop(self):
        self.running = False
        self.socket.close()
        self.context.term()


# ========== ZMQ 命令发送客户端 ==========
class ZmqCmdClient:
    def __init__(self, host, port):
        self.host = host
        self.port = port
        self.context = zmq.Context()
        self.socket = self.context.socket(zmq.DEALER)
        # PUSH socket 用于向 datamock.py 发送标签命令
        self._label_sock = self.context.socket(zmq.PUSH)
        self._label_sock.connect(DATAMOCK_LABEL_ADDR)
        print(f"[Client] label PUSH connected to {DATAMOCK_LABEL_ADDR}")

    def connect(self):
        self.socket.connect(f"tcp://{self.host}:{self.port}")
        print(f"[Client] connected to {self.host}:{self.port}")

    def start_recv_thread(self, result_server):
        """启动后台线程，持续接收 decoder 通过 8099 ROUTER 回发的消息，并更新 result_server 的状态"""
        self._result_server = result_server
        self._stop_recv = threading.Event()

        def _recv_loop():
            while not self._stop_recv.is_set():
                try:
                    frames = self.socket.recv_multipart(zmq.NOBLOCK)
                    # DEALER 收到的格式: [b'', json_bytes]
                    data_bytes = frames[-1]
                    message = json.loads(data_bytes.decode('utf-8'))
                    method = message.get('method')
                    params = message.get('params')
                    ts = datetime.now().strftime('%H:%M:%S.%f')[:-3]
                    print(f"[{ts}] [CmdClient] recv: {method}={params}")
                    if method == 'paradigm':
                        self._result_server.paradigm = params
                        print(f"[{ts}] [CmdClient] paradigm updated -> {params}")
                    elif method == 'result':
                        self._result_server.ChoosenNum = params
                        self._result_server.trial_idx += 1
                        print(f"[{ts}] [CmdClient] result={params} (trial {self._result_server.trial_idx})")
                    elif method == 'energy':
                        self._result_server.energy = params
                except zmq.Again:
                    time.sleep(0.005)
                except Exception as e:
                    print(f"[CmdClient recv] error: {e}")
                    time.sleep(0.01)

        self._recv_thread = threading.Thread(target=_recv_loop, daemon=True)
        self._recv_thread.start()
        print(f"[Client] 后台接收线程已启动（监听 decoder 8099 回发消息）")

    def stop_recv_thread(self):
        if hasattr(self, '_stop_recv'):
            self._stop_recv.set()

    def _send_label(self, label_value):
        """向 datamock.py 发送标签命令"""
        try:
            self._label_sock.send_string(str(label_value), zmq.NOBLOCK)
        except Exception as e:
            print(f"[Client] label send error: {e}")

    def send_data(self, method, params):
        msg = {'method': method, 'params': params}
        try:
            self.socket.send_multipart([b'', json.dumps(msg).encode('utf-8')])
            ts = datetime.now().strftime('%H:%M:%S.%f')[:-3]
            print(f"[{ts}] send_data: {method}={params}")
            # 根据 train/predict 命令向 datamock 发送标签
            if method == 'train':
                if params == 0:
                    self._send_label(1)
                    print(f"[Label] train 0 -> datamock label=1")
                elif params == 1:
                    self._send_label(2)
                    print(f"[Label] train 1 -> datamock label=2")
            elif method == 'predict':
                self._send_label(99)
                print(f"[Label] predict -> datamock label=99")
        except Exception as e:
            print(f"[Client] send error: {e}")


# ========== 主流程 ==========
def run_headless():
    server = ZmqResultServer(port=8088)
    server.start()

    _dh = str(IniRead('system', 'Decoder_Host'))
    _dp = int(IniRead('system', 'Decoder_Port'))
    client = ZmqCmdClient(_dh, _dp)
    client.connect()
    client.start_recv_thread(server)  # 启动后台接收线程，监听 decoder 8099 回发的 paradigm/result 消息

    time.sleep(1)  # 等待连接建立
    client.send_data('decoderClass', 'mi')

    # MI_IntervalEpoch = [0.5, 4.5]，trial时长 = 4.5-0.5 = 4.0s
    _mi_iv = ast.literal_eval(IniRead('system', 'MI_IntervalEpoch'))
    _trial_sec = float(_mi_iv[1] - _mi_iv[0])
    _margin = 1.0
    train_time = max(5.0, _trial_sec + _margin)  # 训练刺激时长（与 MI_main.py 保持一致）

    # MI epoch latency = interval_epoch[1] // 5 = (4.5*250)//5 = 225包 × 20ms = 4.5s
    # train_latency = 225包（MI中 train_latency == latency）
    # 在 train_time 后需再等 epoch_wait 秒，decoder 才能完成 epoch 采集
    epoch_wait = _mi_iv[1] / _mi_iv[1] * (_mi_iv[1] * 250 // 5) * 0.02  # = latency * 20ms
    # 更直接的计算：latency = interval_epoch[1] // 5 = int(4.5*250)//5 = 225，225*0.02 = 4.5s
    epoch_wait = (int(_mi_iv[1] * 250) // 5) * 0.02  # 4.5s

    # predict epoch wait（与 train 相同，MI中 latency == train_latency）
    predict_epoch_wait = epoch_wait  # 4.5s

    test_time = 7.0   # 预测窗口时长（与 MI_main.py 保持一致）
    right_rehabilitation = float(IniRead('system', 'Right_rehabilitation'))
    fault_rehabilitation = float(IniRead('system', 'Fault_rehabilitation'))
    rest_time = float(IniRead('system', 'Rest_time'))

    num_blocks = int(IniRead('system', 'Num_blocks'))
    num_trials = int(IniRead('system', 'Num_trials'))

    trained = 0
    Num_Total = 0
    Num_Success = 0
    user_choice = []

    print("=" * 50)
    print("[Headless] 开始运行 MI 通讯流程（无界面）")
    print(f"  MI_IntervalEpoch={_mi_iv}, trial_sec={_trial_sec:.2f}s")
    print(f"  train_time={train_time:.2f}s, epoch_wait={epoch_wait:.2f}s")
    print(f"  test_time={test_time:.2f}s, predict_epoch_wait={predict_epoch_wait:.2f}s")
    print(f"  num_blocks={num_blocks}, num_trials={num_trials}")
    print("=" * 50)

    try:
        while True:
            # -------- 个体校准阶段 --------
            print("\n[Phase] 个体校准阶段 (paradigm=0)")
            client.send_data('rest', 0)
            time.sleep(1)

            while server.paradigm == 0:
                # 左侧 MI 刺激（train 0，label=1）
                print(f"\n[Train] 左侧 MI 刺激 (train 0) trained={trained}")
                client.send_data('rest', 0)
                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

                trained += 1
                client.send_data('rest', 0)
                time.sleep(1.0)  # 类间休息

                # 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

                trained += 1
                client.send_data('rest', 0)
                time.sleep(1.0)  # 类间休息

            # 个体校准阶段结束
            print("\n[Phase] 个体校准结束，等待模型训练 (paradigm=2) ...")
            trained = 0
            time.sleep(1)

            # 等待模型训练完成 (paradigm=2 -> paradigm=1)
            while server.paradigm == 2:
                print("[Phase] 等待模型训练完成 ...")
                time.sleep(0.5)

            # -------- 康复训练阶段 --------
            while server.paradigm == 1:
                print("\n[Phase] 康复训练阶段 (paradigm=1)")
                for block_idx in range(num_blocks):
                    print(f"\n  [Block {block_idx+1}/{num_blocks}]")
                    time.sleep(10)  # 每轮开始前等待

                    for trial_idx in range(num_trials):
                        print(f"  [Trial {trial_idx+1}/{num_trials}]")

                        time.sleep(0.5)  # ding 提示
                        server.ChoosenNum = -1

                        # 开始预测
                        # MI predict epoch latency = 225包 × 20ms = 4.5s，需额外等待 epoch 完成
                        client.send_data('predict', 1)
                        t_start = time.perf_counter()
                        while time.perf_counter() - t_start < test_time + predict_epoch_wait:
                            if server.ChoosenNum >= 0:
                                Num_Total += 1
                                user_choice.append(server.ChoosenNum)
                                if server.ChoosenNum == 0:
                                    Num_Success += 1
                                    rest_time = right_rehabilitation
                                elif server.ChoosenNum == 1:
                                    rest_time = fault_rehabilitation
                                break
                            time.sleep(0.02)

                        trained += 1
                        client.send_data('rest', 0)
                        time.sleep(0.5)
                        time.sleep(rest_time)
                        server.ChoosenNum = -1

                # 训练结束
                print("\n[Phase] 康复训练结束")
                break  # 退出康复训练循环

            # 统计结果
            overall_accuracy = Num_Success / Num_Total if Num_Total > 0 else 0
            print(f"\n[Result] Overall={overall_accuracy:.3f} ({Num_Success}/{Num_Total})")
            print(f"[Result] user_choice={user_choice}")
            break  # 完成一个完整流程后退出

    except KeyboardInterrupt:
        print("\n[Headless] 用户中断")
    finally:
        client.send_data('predict', 2)   # 关闭系统
        client.send_data('saveData', 0)
        server.stop()
        print("[Headless] 已发送关闭指令，退出。")


if __name__ == '__main__':
    run_headless()