411 lines
19 KiB
Python
411 lines
19 KiB
Python
import tkinter as tk
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from tkinter import ttk
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import time
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import os
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from datetime import datetime, timedelta
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import random
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from collections import deque
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import json # 引入 json
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# --- 引入 Matplotlib ---
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import matplotlib.pyplot as plt
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from matplotlib.figure import Figure
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from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
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import matplotlib.dates as mdates # 用于处理日期格式
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import matplotlib # 导入 matplotlib
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# --- 配置 ---
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LOG_FILE_PATH = os.path.join("logs", "interest", "interest_history.log") # 指向历史日志文件
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REFRESH_INTERVAL_MS = 200 # 刷新间隔 (毫秒) - 可以适当调长，因为读取文件可能耗时
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WINDOW_TITLE = "Interest Monitor (Live History)"
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MAX_HISTORY_POINTS = 1000 # 图表上显示的最大历史点数 (可以增加)
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MAX_STREAMS_TO_DISPLAY = 15 # 最多显示多少个聊天流的折线图 (可以增加)
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# *** 添加 Matplotlib 中文字体配置 ***
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# 尝试使用 'SimHei' 或 'Microsoft YaHei'，如果找不到，matplotlib 会回退到默认字体
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# 确保你的系统上安装了这些字体
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matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei']
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matplotlib.rcParams['axes.unicode_minus'] = False # 解决负号'-'显示为方块的问题
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class InterestMonitorApp:
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def __init__(self, root):
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self.root = root
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self.root.title(WINDOW_TITLE)
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self.root.geometry("1800x800") # 调整窗口大小以适应图表
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# --- 数据存储 ---
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# 使用 deque 来存储有限的历史数据点
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# key: stream_id, value: deque([(timestamp, interest_level), ...])
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self.stream_history = {}
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# key: stream_id, value: deque([(timestamp, reply_probability), ...]) # <--- 新增：存储概率历史
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self.probability_history = {}
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self.stream_colors = {} # 为每个 stream 分配颜色
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self.stream_display_names = {} # *** New: Store display names (group_name) ***
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self.selected_stream_id = tk.StringVar() # 用于 Combobox 绑定
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# --- UI 元素 ---
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# 创建 Notebook (选项卡控件)
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self.notebook = ttk.Notebook(root)
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self.notebook.pack(pady=10, padx=10, fill=tk.BOTH, expand=1)
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# --- 第一个选项卡：所有流 ---
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self.frame_all = ttk.Frame(self.notebook, padding="5 5 5 5")
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self.notebook.add(self.frame_all, text='所有聊天流')
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# 状态标签
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self.status_label = tk.Label(root, text="Initializing...", anchor="w", fg="grey")
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self.status_label.pack(side=tk.BOTTOM, fill=tk.X, padx=5, pady=2)
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# Matplotlib 图表设置 (用于第一个选项卡)
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self.fig = Figure(figsize=(5, 4), dpi=100)
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self.ax = self.fig.add_subplot(111)
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# 配置在 update_plot 中进行，避免重复
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# 创建 Tkinter 画布嵌入 Matplotlib 图表 (用于第一个选项卡)
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self.canvas = FigureCanvasTkAgg(self.fig, master=self.frame_all) # <--- 放入 frame_all
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self.canvas_widget = self.canvas.get_tk_widget()
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self.canvas_widget.pack(side=tk.TOP, fill=tk.BOTH, expand=1)
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# --- 第二个选项卡：单个流 ---
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self.frame_single = ttk.Frame(self.notebook, padding="5 5 5 5")
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self.notebook.add(self.frame_single, text='单个聊天流详情')
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# 单个流选项卡的上部控制区域
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self.control_frame_single = ttk.Frame(self.frame_single)
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self.control_frame_single.pack(side=tk.TOP, fill=tk.X, pady=5)
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ttk.Label(self.control_frame_single, text="选择聊天流:").pack(side=tk.LEFT, padx=(0, 5))
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self.stream_selector = ttk.Combobox(self.control_frame_single, textvariable=self.selected_stream_id, state="readonly", width=50)
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self.stream_selector.pack(side=tk.LEFT, fill=tk.X, expand=True)
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self.stream_selector.bind("<<ComboboxSelected>>", self.on_stream_selected)
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# Matplotlib 图表设置 (用于第二个选项卡)
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self.fig_single = Figure(figsize=(5, 4), dpi=100)
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# 修改：创建两个子图，一个显示兴趣度，一个显示概率
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self.ax_single_interest = self.fig_single.add_subplot(211) # 2行1列的第1个
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self.ax_single_probability = self.fig_single.add_subplot(212, sharex=self.ax_single_interest) # 2行1列的第2个，共享X轴
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# 创建 Tkinter 画布嵌入 Matplotlib 图表 (用于第二个选项卡)
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self.canvas_single = FigureCanvasTkAgg(self.fig_single, master=self.frame_single) # <--- 放入 frame_single
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self.canvas_widget_single = self.canvas_single.get_tk_widget()
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self.canvas_widget_single.pack(side=tk.TOP, fill=tk.BOTH, expand=1)
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# --- 初始化和启动刷新 ---
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self.update_display() # 首次加载并开始刷新循环
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def on_stream_selected(self, event=None):
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"""当 Combobox 选择改变时调用，更新单个流的图表"""
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self.update_single_stream_plot()
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def get_random_color(self):
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"""生成随机颜色用于区分线条"""
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return "#{:06x}".format(random.randint(0, 0xFFFFFF))
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def load_and_update_history(self):
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"""从 history log 文件加载数据并更新历史记录"""
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if not os.path.exists(LOG_FILE_PATH):
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self.set_status(f"Error: Log file not found at {LOG_FILE_PATH}", "red")
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# 如果文件不存在，不清空现有数据，以便显示最后一次成功读取的状态
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return
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# *** Reset display names each time we reload ***
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new_stream_history = {}
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new_stream_display_names = {}
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new_probability_history = {} # <--- 重置概率历史
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read_count = 0
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error_count = 0
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# *** Calculate the timestamp threshold for the last 30 minutes ***
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current_time = time.time()
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time_threshold = current_time - (15 * 60) # 30 minutes in seconds
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try:
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with open(LOG_FILE_PATH, 'r', encoding='utf-8') as f:
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for line in f:
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read_count += 1
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try:
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log_entry = json.loads(line.strip())
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timestamp = log_entry.get("timestamp")
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# *** Add time filtering ***
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if timestamp is None or float(timestamp) < time_threshold:
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continue # Skip old or invalid entries
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stream_id = log_entry.get("stream_id")
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interest_level = log_entry.get("interest_level")
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group_name = log_entry.get("group_name", stream_id) # *** Get group_name, fallback to stream_id ***
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reply_probability = log_entry.get("reply_probability") # <--- 获取概率值
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# *** Check other required fields AFTER time filtering ***
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if stream_id is None or interest_level is None:
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error_count += 1
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continue # 跳过无效行
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# 如果是第一次读到这个 stream_id，则创建 deque
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if stream_id not in new_stream_history:
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new_stream_history[stream_id] = deque(maxlen=MAX_HISTORY_POINTS)
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new_probability_history[stream_id] = deque(maxlen=MAX_HISTORY_POINTS) # <--- 创建概率 deque
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# 检查是否已有颜色，没有则分配
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if stream_id not in self.stream_colors:
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self.stream_colors[stream_id] = self.get_random_color()
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# *** Store the latest display name found for this stream_id ***
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new_stream_display_names[stream_id] = group_name
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# 添加数据点
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new_stream_history[stream_id].append((float(timestamp), float(interest_level)))
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# 添加概率数据点 (如果存在)
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if reply_probability is not None:
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try:
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new_probability_history[stream_id].append((float(timestamp), float(reply_probability)))
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except (TypeError, ValueError):
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# 如果概率值无效，可以跳过或记录一个默认值，这里跳过
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pass
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except json.JSONDecodeError:
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error_count += 1
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# logger.warning(f"Skipping invalid JSON line: {line.strip()}")
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continue # 跳过无法解析的行
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except (TypeError, ValueError) as e:
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error_count += 1
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# logger.warning(f"Skipping line due to data type error ({e}): {line.strip()}")
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continue # 跳过数据类型错误的行
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# 读取完成后，用新数据替换旧数据
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self.stream_history = new_stream_history
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self.stream_display_names = new_stream_display_names # *** Update display names ***
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self.probability_history = new_probability_history # <--- 更新概率历史
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status_msg = f"Data loaded at {datetime.now().strftime('%H:%M:%S')}. Lines read: {read_count}."
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if error_count > 0:
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status_msg += f" Skipped {error_count} invalid lines."
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self.set_status(status_msg, "orange")
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else:
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self.set_status(status_msg, "green")
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except IOError as e:
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self.set_status(f"Error reading file {LOG_FILE_PATH}: {e}", "red")
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except Exception as e:
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self.set_status(f"An unexpected error occurred during loading: {e}", "red")
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# --- 更新 Combobox ---
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self.update_stream_selector()
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def update_stream_selector(self):
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"""更新单个流选项卡中的 Combobox 列表"""
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# 创建 (display_name, stream_id) 对的列表，按 display_name 排序
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available_streams = sorted(
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[(name, sid) for sid, name in self.stream_display_names.items() if sid in self.stream_history and self.stream_history[sid]],
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key=lambda item: item[0] # 按显示名称排序
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)
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# 更新 Combobox 的值 (仅显示 display_name)
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self.stream_selector['values'] = [name for name, sid in available_streams]
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# 检查当前选中的 stream_id 是否仍然有效
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current_selection_name = self.selected_stream_id.get()
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current_selection_valid = any(name == current_selection_name for name, sid in available_streams)
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if not current_selection_valid and available_streams:
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# 如果当前选择无效，并且有可选流，则默认选中第一个
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self.selected_stream_id.set(available_streams[0][0])
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# 手动触发一次更新，因为 set 不会触发 <<ComboboxSelected>>
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self.update_single_stream_plot()
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elif not available_streams:
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# 如果没有可选流，清空选择
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self.selected_stream_id.set("")
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self.update_single_stream_plot() # 清空图表
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def update_all_streams_plot(self):
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"""更新第一个选项卡的 Matplotlib 图表 (显示所有流)"""
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self.ax.clear() # 清除旧图
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# *** 设置中文标题和标签 ***
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self.ax.set_title("兴趣度随时间变化图 (所有活跃流)")
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self.ax.set_xlabel("时间")
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self.ax.set_ylabel("兴趣度")
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self.ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))
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self.ax.grid(True)
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self.ax.set_ylim(0, 10) # 固定 Y 轴范围 0-10
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# 只绘制最新的 N 个 stream (按最后记录的兴趣度排序)
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# 注意：现在是基于文件读取的快照排序，可能不是实时最新
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active_streams = sorted(
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self.stream_history.items(),
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key=lambda item: item[1][-1][1] if item[1] else 0, # 按最后兴趣度排序
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reverse=True
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)[:MAX_STREAMS_TO_DISPLAY]
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all_times = [] # 用于确定 X 轴范围
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for stream_id, history in active_streams:
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if not history:
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continue
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timestamps, interests = zip(*history)
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# 将 time.time() 时间戳转换为 matplotlib 可识别的日期格式
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try:
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mpl_dates = [datetime.fromtimestamp(ts) for ts in timestamps]
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all_times.extend(mpl_dates) # 收集所有时间点
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# *** Use display name for label ***
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display_label = self.stream_display_names.get(stream_id, stream_id)
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self.ax.plot(
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mpl_dates,
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interests,
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label=display_label, # *** Use display_label ***
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color=self.stream_colors.get(stream_id, 'grey'),
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marker='.',
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markersize=3,
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linestyle='-',
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linewidth=1
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)
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except ValueError as e:
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print(f"Skipping plot for {stream_id} due to invalid timestamp: {e}")
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continue
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if all_times:
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# 根据数据动态调整 X 轴范围，留一点边距
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min_time = min(all_times)
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max_time = max(all_times)
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# delta = max_time - min_time
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# self.ax.set_xlim(min_time - delta * 0.05, max_time + delta * 0.05)
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self.ax.set_xlim(min_time, max_time)
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# 自动格式化X轴标签
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self.fig.autofmt_xdate()
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else:
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# 如果没有数据，设置一个默认的时间范围，例如最近一小时
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now = datetime.now()
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one_hour_ago = now - timedelta(hours=1)
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self.ax.set_xlim(one_hour_ago, now)
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# 添加图例
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if active_streams:
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# 调整图例位置和大小
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# 字体已通过全局 matplotlib.rcParams 设置
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self.ax.legend(loc='upper left', bbox_to_anchor=(1.02, 1), borderaxespad=0., fontsize='x-small')
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# 调整布局，确保图例不被裁剪
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self.fig.tight_layout(rect=[0, 0, 0.85, 1]) # 右侧留出空间给图例
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self.canvas.draw() # 重绘画布
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def update_single_stream_plot(self):
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"""更新第二个选项卡的 Matplotlib 图表 (显示单个选定的流)"""
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self.ax_single_interest.clear()
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self.ax_single_probability.clear()
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# 设置子图标题和标签
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self.ax_single_interest.set_title("兴趣度")
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self.ax_single_interest.set_ylabel("兴趣度")
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self.ax_single_interest.grid(True)
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self.ax_single_interest.set_ylim(0, 10) # 固定 Y 轴范围 0-10
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self.ax_single_probability.set_title("回复评估概率")
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self.ax_single_probability.set_xlabel("时间")
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self.ax_single_probability.set_ylabel("概率")
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self.ax_single_probability.grid(True)
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self.ax_single_probability.set_ylim(0, 1.05) # 固定 Y 轴范围 0-1
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self.ax_single_probability.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))
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selected_name = self.selected_stream_id.get()
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selected_sid = None
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# --- 新增：根据选中的名称找到 stream_id ---
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if selected_name:
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for sid, name in self.stream_display_names.items():
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if name == selected_name:
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selected_sid = sid
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break
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all_times = [] # 用于确定 X 轴范围
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# --- 新增：绘制兴趣度图 ---
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if selected_sid and selected_sid in self.stream_history and self.stream_history[selected_sid]:
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history = self.stream_history[selected_sid]
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timestamps, interests = zip(*history)
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try:
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mpl_dates = [datetime.fromtimestamp(ts) for ts in timestamps]
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all_times.extend(mpl_dates)
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self.ax_single_interest.plot(
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mpl_dates,
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interests,
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color=self.stream_colors.get(selected_sid, 'blue'),
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marker='.',
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markersize=3,
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linestyle='-',
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linewidth=1
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)
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except ValueError as e:
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print(f"Skipping interest plot for {selected_sid} due to invalid timestamp: {e}")
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# --- 新增：绘制概率图 ---
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if selected_sid and selected_sid in self.probability_history and self.probability_history[selected_sid]:
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prob_history = self.probability_history[selected_sid]
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prob_timestamps, probabilities = zip(*prob_history)
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try:
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prob_mpl_dates = [datetime.fromtimestamp(ts) for ts in prob_timestamps]
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# 注意：概率图的时间点可能与兴趣度不同，也需要加入 all_times
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all_times.extend(prob_mpl_dates)
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self.ax_single_probability.plot(
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prob_mpl_dates,
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probabilities,
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color=self.stream_colors.get(selected_sid, 'green'), # 可以用不同颜色
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marker='.',
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markersize=3,
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linestyle='-',
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linewidth=1
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)
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except ValueError as e:
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print(f"Skipping probability plot for {selected_sid} due to invalid timestamp: {e}")
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# --- 新增：调整 X 轴范围和格式 ---
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if all_times:
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min_time = min(all_times)
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max_time = max(all_times)
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# 设置共享的 X 轴范围
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self.ax_single_interest.set_xlim(min_time, max_time)
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# self.ax_single_probability.set_xlim(min_time, max_time) # sharex 会自动同步
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# 自动格式化X轴标签 (应用到共享轴的最后一个子图上通常即可)
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self.fig_single.autofmt_xdate()
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else:
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# 如果没有数据，设置一个默认的时间范围
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now = datetime.now()
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one_hour_ago = now - timedelta(hours=1)
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self.ax_single_interest.set_xlim(one_hour_ago, now)
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# self.ax_single_probability.set_xlim(one_hour_ago, now) # sharex 会自动同步
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# --- 新增：重新绘制画布 ---
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self.canvas_single.draw()
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def update_display(self):
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"""主更新循环"""
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try:
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self.load_and_update_history() # 从文件加载数据并更新内部状态
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# *** 修改：分别调用两个图表的更新方法 ***
|
||
self.update_all_streams_plot() # 更新所有流的图表
|
||
self.update_single_stream_plot() # 更新单个流的图表
|
||
except Exception as e:
|
||
# 提供更详细的错误信息
|
||
import traceback
|
||
error_msg = f"Error during update: {e}\n{traceback.format_exc()}"
|
||
self.set_status(error_msg, "red")
|
||
print(error_msg) # 打印详细错误到控制台
|
||
|
||
# 安排下一次刷新
|
||
self.root.after(REFRESH_INTERVAL_MS, self.update_display)
|
||
|
||
def set_status(self, message: str, color: str = "grey"):
|
||
"""更新状态栏标签"""
|
||
# 限制状态栏消息长度
|
||
max_len = 150
|
||
display_message = (message[:max_len] + '...') if len(message) > max_len else message
|
||
self.status_label.config(text=display_message, fg=color)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
# 导入 timedelta 用于默认时间范围
|
||
from datetime import timedelta
|
||
root = tk.Tk()
|
||
app = InterestMonitorApp(root)
|
||
root.mainloop() |