update server
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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自动控制 GUI 界面脚本 - 通过模拟用户操作设置目标压力
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升级功能:
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1. 加入坐标校准功能，摆脱写死的硬编码坐标
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2. 自动寻找并置顶 GUI 窗口
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3. 加入 PyAutoGUI 故障保护 (防失控)
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使用方法:
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1. 首次使用建议进行校准: python auto_test.py --calibrate --targets 50 80 100
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2. 后续固定窗口位置后直接运行: python auto_test.py --targets 50 80 100
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python tool/auto_test.py --calibrate --targets 50 80 100 180 170 130 200 210 270 290 280 250 175 165 100 45
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"""
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import argparse
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import time
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import platform
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import pyautogui
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try:
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import pygetwindow as gw
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except ImportError:
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gw = None
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# 配置 PyAutoGUI
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pyautogui.FAILSAFE = True # 将鼠标移动到屏幕四个角落可紧急停止脚本
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pyautogui.PAUSE = 0.3 # 每个动作后默认停顿 0.3 秒，让 UI 有时间反应
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# 平台相关的全选快捷键：macOS 用 command，Windows/Linux 用 ctrl
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_MODIFIER_KEY = 'command' if platform.system() == 'Darwin' else 'ctrl'
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class GUIController:
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def __init__(self):
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# 默认坐标 (如果不使用 calibrate 模式，将使用这些备用坐标)
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# 注意：这些默认值是错误的，请务必使用 --calibrate 参数校准
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self.input_x, self.input_y = 200, 150
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self.btn_x, self.btn_y = 320, 150
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def activate_window(self, title_keyword="ReinLoop"):
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"""尝试寻找并激活目标窗口（支持部分标题匹配）"""
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if gw is None:
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print("⚠️ 未安装 pygetwindow，请手动确保 GUI 窗口在前台。")
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print(" 安装命令: pip install pygetwindow")
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return
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print(f"正在寻找包含 '{title_keyword}' 的窗口...")
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try:
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windows = gw.getWindowsWithTitle(title_keyword)
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if windows:
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win = windows[0]
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if win.isMinimized:
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win.restore()
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win.activate()
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print(f"✅ 成功激活窗口: {win.title}")
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time.sleep(1) # 等待窗口彻底弹出
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else:
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print(f"⚠️ 未找到包含 '{title_keyword}' 的窗口。")
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print(f" 当前所有窗口列表:")
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all_wins = gw.getAllWindows()
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for w in all_wins:
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if w.title.strip():
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print(f" - {w.title}")
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print(" 请确保 ReinLoop GUI 已打开，或使用 --calibrate 后手动置顶窗口。")
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except Exception as e:
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print(f"⚠️ 窗口激活失败: {e}，请手动将窗口切换到前台。")
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def calibrate(self):
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"""交互式坐标校准，动态获取按钮位置"""
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print("\n" + "=" * 40)
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print("🔧 进入坐标校准模式 (请不要切走窗口)")
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print("=" * 40)
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print("\n👉 请在 5 秒内将鼠标光标移动到【目标压力输入框】中心...")
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for i in range(5, 0, -1):
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print(f"\r倒计时: {i} 秒", end='')
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time.sleep(1)
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self.input_x, self.input_y = pyautogui.position()
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print(f"\n✅ 输入框坐标已记录: ({self.input_x}, {self.input_y})")
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print("\n👉 请在 5 秒内将鼠标光标移动到【设置目标】按钮中心...")
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for i in range(5, 0, -1):
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print(f"\r倒计时: {i} 秒", end='')
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time.sleep(1)
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self.btn_x, self.btn_y = pyautogui.position()
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print(f"\n✅ 按钮坐标已记录: ({self.btn_x}, {self.btn_y})")
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print("=" * 40 + "\n")
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def set_target_pressure(self, target):
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"""模拟用户操作设置目标压力"""
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print(f"▶ 正在设置目标压力: {target} kPa")
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try:
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# 点击输入框
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pyautogui.click(x=self.input_x, y=self.input_y)
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# 全选并删除现有内容（macOS: command+a, Windows/Linux: ctrl+a）
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pyautogui.hotkey(_MODIFIER_KEY, 'a')
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pyautogui.press('backspace')
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# 输入新的目标压力值
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pyautogui.typewrite(str(target))
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# 点击"设置目标"按钮
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pyautogui.click(x=self.btn_x, y=self.btn_y)
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print(f"✅ 成功设置目标压力: {target} kPa")
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return True
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except Exception as e:
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print(f"❌ 设置目标压力失败: {e}")
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return False
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def auto_control(targets, interval, do_calibrate):
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print("=" * 60)
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print("🤖 GUI 自动控制脚本启动")
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print("提示: 运行过程中将鼠标移动到屏幕四个角落即可紧急停止")
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print("=" * 60)
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controller = GUIController()
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controller.activate_window()
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if do_calibrate:
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controller.calibrate()
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else:
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print(
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f"ℹ️ 使用默认坐标 (输入框: {controller.input_x},{controller.input_y} | "
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f"按钮: {controller.btn_x},{controller.btn_y})")
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print("⚠️ 如果点击位置不准确，请使用 --calibrate 参数运行脚本。")
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print("\n3秒后开始自动控制序列...")
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time.sleep(3)
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for i, target in enumerate(targets):
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print(f"\n--- 步骤 {i + 1}/{len(targets)} ---")
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if not controller.set_target_pressure(target):
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print(f"❌ 步骤 {i + 1} 出现异常，提前终止自动控制")
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break
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if i < len(targets) - 1:
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print(f"等待 {interval} 秒...")
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for j in range(interval, 0, -1):
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print(f"\r剩余时间: {j} 秒 ", end='')
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time.sleep(1)
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print()
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print("\n🎉 自动控制序列全部完成！")
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def main():
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parser = argparse.ArgumentParser(description='GUI 自动控制脚本')
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parser.add_argument('--targets', type=float, nargs='+', default=[50, 80, 100, 120],
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help='目标压力值列表，用空格隔开，单位 kPa')
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parser.add_argument('--interval', type=int, default=10,
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help='每个目标压力持续时间，单位秒')
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parser.add_argument('--calibrate', action='store_true',
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help='启动坐标校准模式，动态获取输入框和按钮的屏幕坐标')
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args = parser.parse_args()
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auto_control(args.targets, args.interval, args.calibrate)
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,285 @@
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import os
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import pickle
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import glob
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def load_and_merge_pickle_chunks(folder_path, file_pattern="*.pkl"):
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"""
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从指定文件夹中读取所有匹配的分片文件，解包并合并成一个总的数据列表。
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Args:
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folder_path: 存放 .pkl 分片文件的文件夹路径
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file_pattern: 文件匹配模式，默认匹配所有 .pkl 文件
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"""
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all_episodes = []
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# 获取所有匹配的 pkl 文件路径，并按名称排序（确保 part1, part2 顺序或逻辑清晰）
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search_path = os.path.join(folder_path, file_pattern)
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file_list = sorted(glob.glob(search_path))
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if not file_list:
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print(f"❌ 未在路径 【{folder_path}】 下找到任何匹配 【{file_pattern}】 的文件！")
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return []
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print(f"📂 找到 {len(file_list)} 个数据分片文件，开始加载...")
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for file_path in file_list:
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try:
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with open(file_path, 'rb') as f:
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# 每个分片解包出来都是一个 list [ep1, ep2, ...]
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chunk_data = pickle.load(f)
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if isinstance(chunk_data, list):
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all_episodes.extend(chunk_data)
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print(f" ✅ 成功加载: {os.path.basename(file_path)} (包含 {len(chunk_data)} 个 Episode)")
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else:
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print(f" ⚠️ 警告: {os.path.basename(file_path)} 解析出的数据格式不是列表，跳过。")
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except Exception as e:
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print(f" ❌ 读取文件 {os.path.basename(file_path)} 失败: {e}")
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print(f"整个序列加载完成，共合并了 {len(all_episodes)} 个 Episode。")
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return all_episodes
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def analyze_episodes_data(episode_data_raw):
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"""
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分析 Episode 数据，统计超调情况。
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"""
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total_episodes = len(episode_data_raw)
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if total_episodes == 0:
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print("没有数据可供分析。")
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return
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invalid_count = 0 # 最后一步误差绝对值 > 2 kPa 的无效 episode
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invalid_high_flow = 0 # 无效 episode 中流量 > 200
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invalid_low_flow = 0 # 无效 episode 中流量 < 100
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all_steady_abs_errors = [] # 所有有效 episode 的稳态误差（绝对值）
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no_overshoot_count = 0
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no_overshoot_abs_errors = [] # 绝对值稳态误差
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no_overshoot_raw_errors = [] # 带符号稳态误差（+ = 高于目标, - = 低于目标）
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overshoot_lt_1_count = 0
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overshoot_1_to_2_count = 0
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overshoot_2_to_3_count = 0
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overshoot_3_to_4_count = 0
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overshoot_4_to_5_count = 0
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overshoot_5_to_10_count = 0
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overshoot_gt_10_count = 0
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overshoots_5_to_10 = []
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overshoots_gt_10 = []
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for idx, ep in enumerate(episode_data_raw):
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pressures = ep.get('pressures', [])
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target_p = ep.get('target_pressure', 0.0)
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if not pressures:
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continue
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# 最后一步误差绝对值 > 2 kPa → 无效 episode，跳过
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errors = ep.get('errors', [])
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if errors and abs(errors[-1]) > 2:
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invalid_count += 1
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q = ep.get('Q_in', 0)
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if q > 200:
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invalid_high_flow += 1
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elif q < 100:
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invalid_low_flow += 1
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continue
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initial_p = pressures[0]
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# 所有有效 episode 的稳态误差（最后 30 步绝对值均值）
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if errors:
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last_n = errors[-30:] if len(errors) >= 30 else errors
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all_steady_abs_errors.append(sum(abs(e) for e in last_n) / len(last_n))
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is_step_up = target_p >= initial_p # 升压为 True，降压为 False
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overshoot = 0.0
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if is_step_up:
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# 升压：最大值大于目标压力为超调
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max_p = max(pressures)
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if max_p > target_p:
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overshoot = max_p - target_p
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else:
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# 降压：最小值小于目标压力为超调
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min_p = min(pressures)
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if min_p < target_p:
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overshoot = target_p - min_p
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# 统计区间
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if overshoot == 0:
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no_overshoot_count += 1
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elif overshoot < 1.0:
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overshoot_lt_1_count += 1
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# 最后 30 步的平均误差作为稳态误差（分别记录绝对值和带符号值）
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if len(errors) >= 30:
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last_30 = errors[-30:]
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elif errors:
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last_30 = errors
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else:
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last_30 = []
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if last_30:
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no_overshoot_abs_errors.append(sum(abs(e) for e in last_30) / len(last_30))
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no_overshoot_raw_errors.append(sum(last_30) / len(last_30))
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elif 1.0 <= overshoot < 2.0:
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overshoot_1_to_2_count += 1
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elif 2.0 <= overshoot < 3.0:
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overshoot_2_to_3_count += 1
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elif 3.0 <= overshoot < 4.0:
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overshoot_3_to_4_count += 1
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elif 4.0 <= overshoot <= 5.0:
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overshoot_4_to_5_count += 1
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else:
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item = {
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"index": idx,
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"direction": "升压" if is_step_up else "降压",
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"initial_p": initial_p,
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"target_p": target_p,
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"overshoot_value": round(overshoot, 3),
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"Q_in": ep.get("Q_in", 0),
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}
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if overshoot <= 10.0:
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overshoot_5_to_10_count += 1
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overshoots_5_to_10.append(item)
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else:
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overshoot_gt_10_count += 1
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overshoots_gt_10.append(item)
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# 打印报告
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def _pct(n): return f"{n / total_episodes * 100:.1f}%"
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print("\n" + "="*25 + " 离线数据分析 " + "="*25)
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valid_episodes = total_episodes - invalid_count
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print(f"合并后的总 Episode 数 : {total_episodes}")
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print(f" - 无效 Episode（末步误差>2）: {invalid_count} ({_pct(invalid_count)})")
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if invalid_count > 0:
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print(f" ├ 流量 > 200 L/min : {invalid_high_flow}")
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print(f" └ 流量 < 100 L/min : {invalid_low_flow}")
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print(f" - 有效 Episode 数 : {valid_episodes}")
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print(f" - 未超调的 Episode 数 : {no_overshoot_count} ({_pct(no_overshoot_count)})")
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print(f" - 超调 < 1 kPa : {overshoot_lt_1_count} ({_pct(overshoot_lt_1_count)})")
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print(f" - 超调在 1 ~ 2 kPa 之间 : {overshoot_1_to_2_count} ({_pct(overshoot_1_to_2_count)})")
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print(f" - 超调在 2 ~ 3 kPa 之间 : {overshoot_2_to_3_count} ({_pct(overshoot_2_to_3_count)})")
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print(f" - 超调在 3 ~ 4 kPa 之间 : {overshoot_3_to_4_count} ({_pct(overshoot_3_to_4_count)})")
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print(f" - 超调在 4 ~ 5 kPa 之间 : {overshoot_4_to_5_count} ({_pct(overshoot_4_to_5_count)})")
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print(f" - 超调在 5 ~ 10 kPa 之间 : {overshoot_5_to_10_count} ({_pct(overshoot_5_to_10_count)})")
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print(f" - 超调 > 10 kPa : {overshoot_gt_10_count} ({_pct(overshoot_gt_10_count)})")
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print("=" * 68)
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def _print_detail(title, items):
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if items:
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print(f"\n[⚠️ {title}]:")
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for item in items:
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print(f" * Episode [{item['index']}] ({item['direction']}): "
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f"初始 {item['initial_p']:.2f} -> 目标 {item['target_p']:.2f} | "
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f"超调量: {item['overshoot_value']:.2f} kPa | "
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f"流量: {item['Q_in']:.1f} L/min")
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_print_detail("超调在 5 ~ 10 kPa", overshoots_5_to_10)
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_print_detail("超调大于 10 kPa", overshoots_gt_10)
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if not overshoots_5_to_10 and not overshoots_gt_10:
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print("\n🎉 极好！没有发现超调大于 5 kPa 的数据。")
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# ---- 流量分布统计 ----
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flow_bins = [
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(0, 10), (10, 50), (50, 100), (100, 150),
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(150, 200), (200, 250), (250, 300),
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]
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flow_counts = {f"{lo}~{hi}": 0 for lo, hi in flow_bins}
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flow_counts["300+"] = 0
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for ep in episode_data_raw:
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q = ep.get('Q_in', 0)
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placed = False
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for lo, hi in flow_bins:
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if lo <= q < hi:
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flow_counts[f"{lo}~{hi}"] += 1
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placed = True
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break
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if not placed:
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flow_counts["300+"] += 1
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print(f"\n📊 流量分布统计 (共 {total_episodes} 个 Episode):")
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for lo, hi in flow_bins:
|
||||
label = f"{lo}~{hi}"
|
||||
print(f" {label:>10} L/min : {flow_counts[label]:>5} ({flow_counts[label]/total_episodes*100:5.1f}%)")
|
||||
print(f" {'300+':>10} L/min : {flow_counts['300+']:>5} ({flow_counts['300+']/total_episodes*100:5.1f}%)")
|
||||
|
||||
if all_steady_abs_errors:
|
||||
avg_all = sum(all_steady_abs_errors) / len(all_steady_abs_errors)
|
||||
print(f"\n📊 所有有效 Episode 平均稳态误差（最后 30 步绝对值均值）: {avg_all:.3f} kPa"
|
||||
f" ({len(all_steady_abs_errors)} 个 Episode)")
|
||||
|
||||
if no_overshoot_abs_errors:
|
||||
avg_abs = sum(no_overshoot_abs_errors) / len(no_overshoot_abs_errors)
|
||||
avg_raw = sum(no_overshoot_raw_errors) / len(no_overshoot_raw_errors)
|
||||
print(f"\n📊 超调0～1kpa Episode 平均稳态误差（最后 30 步）:")
|
||||
print(f" 绝对值均值 : {avg_abs:.3f} kPa")
|
||||
print(f" 带符号均值 : {avg_raw:.3f} kPa ({'偏高于目标' if avg_raw > 0 else '偏低' if avg_raw < 0 else '无偏'})"
|
||||
f" ({no_overshoot_count} 个 Episode)")
|
||||
|
||||
|
||||
def print_episode_detail(episode_data_raw, index):
|
||||
"""打印指定 episode 的完整数据"""
|
||||
if index < 0 or index >= len(episode_data_raw):
|
||||
print(f"❌ Episode 索引 {index} 超出范围 (0~{len(episode_data_raw)-1})")
|
||||
return
|
||||
|
||||
ep = episode_data_raw[index]
|
||||
print(f"\n{'='*60}")
|
||||
print(f" Episode [{index}] 完整数据")
|
||||
print(f"{'='*60}")
|
||||
|
||||
for key in ['Q_in', 'volume', 'target_pressure', 'mode']:
|
||||
if key in ep:
|
||||
print(f" {key}: {ep[key]}")
|
||||
|
||||
pressures = ep.get('pressures', [])
|
||||
errors = ep.get('errors', [])
|
||||
valve_openings = ep.get('valves', [])
|
||||
|
||||
print(f"\n 步数: {len(pressures)}")
|
||||
if pressures:
|
||||
print(f" 初始压力: {pressures[0]:.2f} kPa")
|
||||
print(f" 最终压力: {pressures[-1]:.2f} kPa")
|
||||
print(f" 目标压力: {ep.get('target_pressure', 'N/A')} kPa")
|
||||
if errors:
|
||||
print(f" 最终误差: {errors[-1]:.3f} kPa")
|
||||
|
||||
print(f"\n {'步':>4s} {'压力(kPa)':>10s} {'误差(kPa)':>10s} {'开度(%)':>8s}")
|
||||
print(f" {'-'*36}")
|
||||
n = len(pressures)
|
||||
for i in range(n):
|
||||
p = pressures[i]
|
||||
e = errors[i] if i < len(errors) else float('nan')
|
||||
vo = valve_openings[i] if i < len(valve_openings) else float('nan')
|
||||
print(f" {i:4d} {p:10.2f} {e:10.3f} {vo:8.2f}")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
|
||||
# --- 执行离线分析 ---
|
||||
if __name__ == "__main__":
|
||||
# 💡 数据存放文件夹路径
|
||||
DATA_FOLDER = "/Users/menglingrui/Documents/DominatedConvergence/cloud_down_file/永久/data_8L"
|
||||
|
||||
# 1. 读取并合并分片
|
||||
merged_data = load_and_merge_pickle_chunks(DATA_FOLDER, file_pattern="*part*.pkl")
|
||||
|
||||
# 2. 执行分析
|
||||
if merged_data:
|
||||
analyze_episodes_data(merged_data)
|
||||
# 3. 找出无效 episode（末步误差绝对值 > 2 kPa），打印前 3 个的完整数据
|
||||
# invalid_indices = []
|
||||
# for idx, ep in enumerate(merged_data):
|
||||
# errors = ep.get('errors', [])
|
||||
# if errors and abs(errors[-1]) > 2:
|
||||
# invalid_indices.append(idx)
|
||||
# if len(invalid_indices) >= 3:
|
||||
# break
|
||||
# if invalid_indices:
|
||||
# print(f"\n找到 {len(invalid_indices)} 个无效 Episode，索引: {invalid_indices}")
|
||||
# for idx in invalid_indices:
|
||||
# print_episode_detail(merged_data, idx)
|
||||
# else:
|
||||
# print("\n未找到无效 Episode")
|
||||
print_episode_detail(merged_data, 2500)
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,10 @@
|
||||
parameter,value
|
||||
q_in_val,50.0
|
||||
dt,0.1
|
||||
n_order,6
|
||||
t_c,2.5
|
||||
levels,"10,20,30,40,50,60,70,80"
|
||||
dead_area,240.0
|
||||
xa_full,1000.0
|
||||
V_val,5.0
|
||||
repeat,2
|
||||
|
@@ -0,0 +1,61 @@
|
||||
import matplotlib
|
||||
import shutil
|
||||
import os
|
||||
|
||||
# 获取 Matplotlib 缓存目录
|
||||
cache_dir = matplotlib.get_cachedir()
|
||||
print(f"正在清理缓存目录: {cache_dir}")
|
||||
|
||||
# 删除缓存
|
||||
if os.path.exists(cache_dir):
|
||||
shutil.rmtree(cache_dir)
|
||||
print("字体缓存已清除！请重新运行你的主程序。")
|
||||
else:
|
||||
print("未找到缓存目录。")
|
||||
|
||||
import os
|
||||
import matplotlib
|
||||
|
||||
matplotlib.use('TkAgg')
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.font_manager as fm
|
||||
|
||||
|
||||
# ----------------- 强制解决中文乱码 (Mac版) -----------------
|
||||
def force_chinese_font_mac():
|
||||
"""强制加载 macOS 系统自带的苹方或黑体"""
|
||||
# macOS 常见中文字体路径
|
||||
font_paths = [
|
||||
"/System/Library/Fonts/PingFang.ttc", # 苹方 (现代 macOS 默认中文字体)
|
||||
"/System/Library/Fonts/STHeiti Light.ttc", # 华文黑体
|
||||
"/System/Library/Fonts/STHeiti Medium.ttc", # 华文黑体 (中等粗细)
|
||||
"/System/Library/Fonts/Supplemental/Songti.ttc", # 宋体 (部分较新 macOS 系统的路径)
|
||||
"/Library/Fonts/Arial Unicode.ttf" # 包含中文的通用字体
|
||||
]
|
||||
|
||||
font_loaded = False
|
||||
for path in font_paths:
|
||||
if os.path.exists(path):
|
||||
try:
|
||||
# 强制将字体加入 Matplotlib 的内存库
|
||||
fm.fontManager.addfont(path)
|
||||
# 获取该字体在 matplotlib 内部的真实名称
|
||||
prop = fm.FontProperties(fname=path)
|
||||
plt.rcParams['font.family'] = prop.get_name()
|
||||
font_loaded = True
|
||||
print(f"已成功加载 Mac 系统字体: {path}")
|
||||
break # 加载成功一个就跳出
|
||||
except Exception as e:
|
||||
print(f"尝试加载字体 {path} 失败: {e}")
|
||||
continue
|
||||
|
||||
if not font_loaded:
|
||||
print("警告: 未在 macOS 默认路径找到中文字体文件。")
|
||||
|
||||
# 解决负号 '-' 显示为方块的问题
|
||||
plt.rcParams['axes.unicode_minus'] = False
|
||||
|
||||
|
||||
# 立即执行字体加载
|
||||
force_chinese_font_mac()
|
||||
# ----------------------------------------------------
|
||||
@@ -0,0 +1,26 @@
|
||||
"""已迁移至 ControlPanel 的辨识反馈管理能力。"""
|
||||
|
||||
import argparse
|
||||
def submit_feedback(customer: str, result: int, run_id=None, timeout=20):
|
||||
raise RuntimeError("辨识反馈已迁移至 ControlPanel，客户端不提供管理接口")
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="提交辨识结果 0/1")
|
||||
parser.add_argument("customer", help="许可证中的客户名称")
|
||||
parser.add_argument("result", type=int, choices=(0, 1), help="1=通过，0=未通过")
|
||||
parser.add_argument("--run-id", help="可选：限定当前辨识 CSV 文件名")
|
||||
args = parser.parse_args()
|
||||
|
||||
try:
|
||||
data = submit_feedback(args.customer, args.result, args.run_id)
|
||||
except Exception as exc:
|
||||
print(f"提交失败: {exc}")
|
||||
return 1
|
||||
state = "已通过" if data["result"] == 1 else "未通过"
|
||||
print(f"提交成功：{state}，runId={data.get('runId', '')}")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -0,0 +1,10 @@
|
||||
{
|
||||
"q_in_val": 50.0,
|
||||
"dt": 0.05,
|
||||
"p_max": 200.0,
|
||||
"fit_low": 50.0,
|
||||
"fit_high": 150.0,
|
||||
"T_delta": 30.0,
|
||||
"xa_full": 1000.0,
|
||||
"num_runs": 3
|
||||
}
|
||||
Reference in New Issue
Block a user