Ruff fix
This commit is contained in:
墨梓柒
@@ -1,4 +1,3 @@
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import time
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import sys
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import os
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import matplotlib.pyplot as plt
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@@ -6,16 +5,17 @@ import matplotlib.dates as mdates
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from datetime import datetime
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from typing import List, Tuple
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import numpy as np
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from src.common.database.database_model import Expression, ChatStreams
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# Add project root to Python path
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project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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sys.path.insert(0, project_root)
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from src.common.database.database_model import Expression, ChatStreams
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# 设置中文字体
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plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
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plt.rcParams['axes.unicode_minus'] = False
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plt.rcParams["font.sans-serif"] = ["SimHei", "Microsoft YaHei", "DejaVu Sans"]
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plt.rcParams["axes.unicode_minus"] = False
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def get_chat_name(chat_id: str) -> str:
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@@ -39,19 +39,14 @@ def get_expression_data() -> List[Tuple[float, float, str, str]]:
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"""获取Expression表中的数据，返回(create_date, count, chat_id, expression_type)的列表"""
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expressions = Expression.select()
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data = []
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for expr in expressions:
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# 如果create_date为空，跳过该记录
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if expr.create_date is None:
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continue
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data.append((
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expr.create_date,
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expr.count,
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expr.chat_id,
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expr.type
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))
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data.append((expr.create_date, expr.count, expr.chat_id, expr.type))
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return data
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@@ -60,71 +55,71 @@ def create_scatter_plot(data: List[Tuple[float, float, str, str]], save_path: st
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if not data:
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print("没有找到有效的表达式数据")
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return
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# 分离数据
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create_dates = [item[0] for item in data]
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counts = [item[1] for item in data]
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chat_ids = [item[2] for item in data]
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expression_types = [item[3] for item in data]
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_chat_ids = [item[2] for item in data]
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_expression_types = [item[3] for item in data]
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# 转换时间戳为datetime对象
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dates = [datetime.fromtimestamp(ts) for ts in create_dates]
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# 计算时间跨度，自动调整显示格式
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time_span = max(dates) - min(dates)
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if time_span.days > 30: # 超过30天，按月显示
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date_format = '%Y-%m-%d'
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date_format = "%Y-%m-%d"
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major_locator = mdates.MonthLocator()
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minor_locator = mdates.DayLocator(interval=7)
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elif time_span.days > 7: # 超过7天，按天显示
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date_format = '%Y-%m-%d'
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date_format = "%Y-%m-%d"
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major_locator = mdates.DayLocator(interval=1)
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minor_locator = mdates.HourLocator(interval=12)
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else: # 7天内，按小时显示
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date_format = '%Y-%m-%d %H:%M'
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date_format = "%Y-%m-%d %H:%M"
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major_locator = mdates.HourLocator(interval=6)
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minor_locator = mdates.HourLocator(interval=1)
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# 创建图形
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fig, ax = plt.subplots(figsize=(12, 8))
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# 创建散点图
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scatter = ax.scatter(dates, counts, alpha=0.6, s=30, c=range(len(dates)), cmap='viridis')
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scatter = ax.scatter(dates, counts, alpha=0.6, s=30, c=range(len(dates)), cmap="viridis")
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# 设置标签和标题
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ax.set_xlabel('创建日期 (Create Date)', fontsize=12)
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ax.set_ylabel('使用次数 (Count)', fontsize=12)
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ax.set_title('表达式使用次数随时间分布散点图', fontsize=14, fontweight='bold')
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ax.set_xlabel("创建日期 (Create Date)", fontsize=12)
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ax.set_ylabel("使用次数 (Count)", fontsize=12)
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ax.set_title("表达式使用次数随时间分布散点图", fontsize=14, fontweight="bold")
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# 设置x轴日期格式 - 根据时间跨度自动调整
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ax.xaxis.set_major_formatter(mdates.DateFormatter(date_format))
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ax.xaxis.set_major_locator(major_locator)
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ax.xaxis.set_minor_locator(minor_locator)
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plt.xticks(rotation=45)
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# 添加网格
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ax.grid(True, alpha=0.3)
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# 添加颜色条
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cbar = plt.colorbar(scatter)
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cbar.set_label('数据点顺序', fontsize=10)
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cbar.set_label("数据点顺序", fontsize=10)
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# 调整布局
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plt.tight_layout()
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# 显示统计信息
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print(f"\n=== 数据统计 ===")
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print("\n=== 数据统计 ===")
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print(f"总数据点数量: {len(data)}")
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print(f"时间范围: {min(dates).strftime('%Y-%m-%d %H:%M:%S')} 到 {max(dates).strftime('%Y-%m-%d %H:%M:%S')}")
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print(f"使用次数范围: {min(counts):.1f} 到 {max(counts):.1f}")
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print(f"平均使用次数: {np.mean(counts):.2f}")
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print(f"中位数使用次数: {np.median(counts):.2f}")
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# 保存图片
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if save_path:
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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plt.savefig(save_path, dpi=300, bbox_inches="tight")
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print(f"\n散点图已保存到: {save_path}")
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# 显示图片
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plt.show()
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@@ -134,7 +129,7 @@ def create_grouped_scatter_plot(data: List[Tuple[float, float, str, str]], save_
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if not data:
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print("没有找到有效的表达式数据")
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return
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# 按chat_id分组
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chat_groups = {}
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for item in data:
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@@ -142,75 +137,82 @@ def create_grouped_scatter_plot(data: List[Tuple[float, float, str, str]], save_
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if chat_id not in chat_groups:
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chat_groups[chat_id] = []
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chat_groups[chat_id].append(item)
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# 计算时间跨度，自动调整显示格式
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all_dates = [datetime.fromtimestamp(item[0]) for item in data]
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time_span = max(all_dates) - min(all_dates)
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if time_span.days > 30: # 超过30天，按月显示
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date_format = '%Y-%m-%d'
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date_format = "%Y-%m-%d"
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major_locator = mdates.MonthLocator()
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minor_locator = mdates.DayLocator(interval=7)
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elif time_span.days > 7: # 超过7天，按天显示
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date_format = '%Y-%m-%d'
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date_format = "%Y-%m-%d"
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major_locator = mdates.DayLocator(interval=1)
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minor_locator = mdates.HourLocator(interval=12)
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else: # 7天内，按小时显示
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date_format = '%Y-%m-%d %H:%M'
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date_format = "%Y-%m-%d %H:%M"
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major_locator = mdates.HourLocator(interval=6)
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minor_locator = mdates.HourLocator(interval=1)
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# 创建图形
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fig, ax = plt.subplots(figsize=(14, 10))
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# 为每个聊天分配不同颜色
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colors = plt.cm.Set3(np.linspace(0, 1, len(chat_groups)))
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for i, (chat_id, chat_data) in enumerate(chat_groups.items()):
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create_dates = [item[0] for item in chat_data]
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counts = [item[1] for item in chat_data]
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dates = [datetime.fromtimestamp(ts) for ts in create_dates]
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chat_name = get_chat_name(chat_id)
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# 截断过长的聊天名称
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display_name = chat_name[:20] + "..." if len(chat_name) > 20 else chat_name
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ax.scatter(dates, counts, alpha=0.7, s=40,
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c=[colors[i]], label=f"{display_name} ({len(chat_data)}个)",
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edgecolors='black', linewidth=0.5)
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ax.scatter(
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dates,
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counts,
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alpha=0.7,
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s=40,
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c=[colors[i]],
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label=f"{display_name} ({len(chat_data)}个)",
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edgecolors="black",
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linewidth=0.5,
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)
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# 设置标签和标题
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ax.set_xlabel('创建日期 (Create Date)', fontsize=12)
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ax.set_ylabel('使用次数 (Count)', fontsize=12)
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ax.set_title('按聊天分组的表达式使用次数散点图', fontsize=14, fontweight='bold')
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ax.set_xlabel("创建日期 (Create Date)", fontsize=12)
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ax.set_ylabel("使用次数 (Count)", fontsize=12)
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ax.set_title("按聊天分组的表达式使用次数散点图", fontsize=14, fontweight="bold")
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# 设置x轴日期格式 - 根据时间跨度自动调整
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ax.xaxis.set_major_formatter(mdates.DateFormatter(date_format))
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ax.xaxis.set_major_locator(major_locator)
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ax.xaxis.set_minor_locator(minor_locator)
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plt.xticks(rotation=45)
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# 添加图例
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ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=8)
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ax.legend(bbox_to_anchor=(1.05, 1), loc="upper left", fontsize=8)
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# 添加网格
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ax.grid(True, alpha=0.3)
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# 调整布局
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plt.tight_layout()
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# 显示统计信息
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print(f"\n=== 分组统计 ===")
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print("\n=== 分组统计 ===")
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print(f"总聊天数量: {len(chat_groups)}")
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for chat_id, chat_data in chat_groups.items():
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chat_name = get_chat_name(chat_id)
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counts = [item[1] for item in chat_data]
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print(f"{chat_name}: {len(chat_data)}个表达式, 平均使用次数: {np.mean(counts):.2f}")
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# 保存图片
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if save_path:
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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plt.savefig(save_path, dpi=300, bbox_inches="tight")
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print(f"\n分组散点图已保存到: {save_path}")
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# 显示图片
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plt.show()
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@@ -220,7 +222,7 @@ def create_type_scatter_plot(data: List[Tuple[float, float, str, str]], save_pat
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if not data:
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print("没有找到有效的表达式数据")
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return
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# 按type分组
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type_groups = {}
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for item in data:
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@@ -228,69 +230,76 @@ def create_type_scatter_plot(data: List[Tuple[float, float, str, str]], save_pat
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if expr_type not in type_groups:
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type_groups[expr_type] = []
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type_groups[expr_type].append(item)
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# 计算时间跨度，自动调整显示格式
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all_dates = [datetime.fromtimestamp(item[0]) for item in data]
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time_span = max(all_dates) - min(all_dates)
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if time_span.days > 30: # 超过30天，按月显示
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date_format = '%Y-%m-%d'
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date_format = "%Y-%m-%d"
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major_locator = mdates.MonthLocator()
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minor_locator = mdates.DayLocator(interval=7)
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elif time_span.days > 7: # 超过7天，按天显示
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date_format = '%Y-%m-%d'
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date_format = "%Y-%m-%d"
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major_locator = mdates.DayLocator(interval=1)
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minor_locator = mdates.HourLocator(interval=12)
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else: # 7天内，按小时显示
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date_format = '%Y-%m-%d %H:%M'
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date_format = "%Y-%m-%d %H:%M"
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major_locator = mdates.HourLocator(interval=6)
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minor_locator = mdates.HourLocator(interval=1)
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# 创建图形
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fig, ax = plt.subplots(figsize=(12, 8))
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# 为每个类型分配不同颜色
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colors = plt.cm.tab10(np.linspace(0, 1, len(type_groups)))
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for i, (expr_type, type_data) in enumerate(type_groups.items()):
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create_dates = [item[0] for item in type_data]
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counts = [item[1] for item in type_data]
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dates = [datetime.fromtimestamp(ts) for ts in create_dates]
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ax.scatter(dates, counts, alpha=0.7, s=40,
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c=[colors[i]], label=f"{expr_type} ({len(type_data)}个)",
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edgecolors='black', linewidth=0.5)
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ax.scatter(
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dates,
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counts,
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alpha=0.7,
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s=40,
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c=[colors[i]],
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label=f"{expr_type} ({len(type_data)}个)",
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edgecolors="black",
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linewidth=0.5,
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)
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# 设置标签和标题
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ax.set_xlabel('创建日期 (Create Date)', fontsize=12)
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ax.set_ylabel('使用次数 (Count)', fontsize=12)
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ax.set_title('按表达式类型分组的散点图', fontsize=14, fontweight='bold')
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ax.set_xlabel("创建日期 (Create Date)", fontsize=12)
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ax.set_ylabel("使用次数 (Count)", fontsize=12)
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ax.set_title("按表达式类型分组的散点图", fontsize=14, fontweight="bold")
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# 设置x轴日期格式 - 根据时间跨度自动调整
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ax.xaxis.set_major_formatter(mdates.DateFormatter(date_format))
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ax.xaxis.set_major_locator(major_locator)
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ax.xaxis.set_minor_locator(minor_locator)
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plt.xticks(rotation=45)
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# 添加图例
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ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
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ax.legend(bbox_to_anchor=(1.05, 1), loc="upper left")
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# 添加网格
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ax.grid(True, alpha=0.3)
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# 调整布局
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plt.tight_layout()
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# 显示统计信息
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print(f"\n=== 类型统计 ===")
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print("\n=== 类型统计 ===")
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for expr_type, type_data in type_groups.items():
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counts = [item[1] for item in type_data]
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print(f"{expr_type}: {len(type_data)}个表达式, 平均使用次数: {np.mean(counts):.2f}")
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# 保存图片
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if save_path:
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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plt.savefig(save_path, dpi=300, bbox_inches="tight")
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print(f"\n类型散点图已保存到: {save_path}")
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# 显示图片
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plt.show()
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@@ -298,35 +307,35 @@ def create_type_scatter_plot(data: List[Tuple[float, float, str, str]], save_pat
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def main():
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"""主函数"""
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print("开始分析表达式数据...")
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# 获取数据
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data = get_expression_data()
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if not data:
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print("没有找到有效的表达式数据（create_date不为空的数据）")
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return
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print(f"找到 {len(data)} 条有效数据")
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# 创建输出目录
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output_dir = os.path.join(project_root, "data", "temp")
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os.makedirs(output_dir, exist_ok=True)
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# 生成时间戳用于文件名
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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# 1. 创建基础散点图
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print("\n1. 创建基础散点图...")
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create_scatter_plot(data, os.path.join(output_dir, f"expression_scatter_{timestamp}.png"))
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# 2. 创建按聊天分组的散点图
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print("\n2. 创建按聊天分组的散点图...")
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create_grouped_scatter_plot(data, os.path.join(output_dir, f"expression_scatter_by_chat_{timestamp}.png"))
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# 3. 创建按类型分组的散点图
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print("\n3. 创建按类型分组的散点图...")
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create_type_scatter_plot(data, os.path.join(output_dir, f"expression_scatter_by_type_{timestamp}.png"))
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print("\n分析完成！")
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