@@ -1050,17 +764,10 @@ class StatisticOutputTask(AsyncTask):
]
tab_content_list.append(
- _format_stat_data(stat["all_time"], "all_time", datetime.fromtimestamp(local_storage["deploy_time"]))
+ _format_stat_data(stat["all_time"], "all_time", datetime.fromtimestamp(local_storage["deploy_time"])) # type: ignore
)
- # 添加Focus统计内容
- focus_tab = self._generate_focus_tab(stat)
- tab_content_list.append(focus_tab)
-
- # 添加版本对比内容
- versions_tab = self._generate_versions_tab(stat)
- tab_content_list.append(versions_tab)
-
+ # 不再添加版本对比内容
# 添加图表内容
chart_data = self._generate_chart_data(stat)
tab_content_list.append(self._generate_chart_tab(chart_data))
@@ -1211,619 +918,6 @@ class StatisticOutputTask(AsyncTask):
with open(self.record_file_path, "w", encoding="utf-8") as f:
f.write(html_template)
- def _generate_focus_tab(self, stat: dict[str, Any]) -> str:
- """生成Focus统计独立分页的HTML内容"""
-
- # 为每个时间段准备Focus数据
- focus_sections = []
-
- for period_name, period_delta, period_desc in self.stat_period:
- stat_data = stat.get(period_name, {})
-
- if stat_data.get(FOCUS_TOTAL_CYCLES, 0) <= 0:
- continue
-
- # 生成Focus统计数据行
- focus_action_rows = ""
- focus_chat_rows = ""
- focus_stage_rows = ""
- focus_action_stage_rows = ""
-
- # Action类型统计
- total_actions = sum(stat_data[FOCUS_ACTION_RATIOS].values()) if stat_data[FOCUS_ACTION_RATIOS] else 0
- if total_actions > 0:
- focus_action_rows = "\n".join(
- [
- f"
| {action_type} | {count} | {(count / total_actions * 100):.1f}% |
"
- for action_type, count in sorted(stat_data[FOCUS_ACTION_RATIOS].items())
- ]
- )
-
- # 按聊天流统计（横向表格，显示各阶段时间差异和不同action的平均时间）
- focus_chat_rows = ""
- if stat_data[FOCUS_AVG_TIMES_BY_CHAT_ACTION]:
- # 获取前三个阶段（不包括执行动作）
- basic_stages = ["观察", "并行调整动作、处理", "规划器"]
- existing_basic_stages = []
- for stage in basic_stages:
- # 检查是否有任何聊天流在这个阶段有数据
- stage_exists = False
- for _chat_id, stage_times in stat_data[FOCUS_AVG_TIMES_BY_CHAT_ACTION].items():
- if stage in stage_times:
- stage_exists = True
- break
- if stage_exists:
- existing_basic_stages.append(stage)
-
- # 获取所有action类型（按出现频率排序）
- all_action_types = sorted(
- stat_data[FOCUS_ACTION_RATIOS].keys(), key=lambda x: stat_data[FOCUS_ACTION_RATIOS][x], reverse=True
- )
-
- # 为每个聊天流生成一行
- chat_rows = []
- for chat_id in sorted(
- stat_data[FOCUS_CYCLE_CNT_BY_CHAT].keys(),
- key=lambda x: stat_data[FOCUS_CYCLE_CNT_BY_CHAT][x],
- reverse=True,
- ):
- chat_name = self.name_mapping.get(chat_id, (chat_id, 0))[0]
- cycle_count = stat_data[FOCUS_CYCLE_CNT_BY_CHAT][chat_id]
-
- # 获取该聊天流的各阶段平均时间
- stage_times = stat_data[FOCUS_AVG_TIMES_BY_CHAT_ACTION].get(chat_id, {})
-
- row_cells = [f"
{chat_name}
({cycle_count}次循环) | "]
-
- # 添加基础阶段时间
- for stage in existing_basic_stages:
- time_val = stage_times.get(stage, 0.0)
- row_cells.append(f"
{time_val:.3f}秒 | ")
-
- # 添加每个action类型的平均执行时间
- for action_type in all_action_types:
- # 使用真实的按聊天流+action类型分组的执行时间数据
- exec_times_by_chat_action = stat_data.get("focus_exec_times_by_chat_action", {})
- chat_action_times = exec_times_by_chat_action.get(chat_id, {})
- avg_exec_time = chat_action_times.get(action_type, 0.0)
-
- if avg_exec_time > 0:
- row_cells.append(f"
{avg_exec_time:.3f}秒 | ")
- else:
- row_cells.append("
- | ")
-
- chat_rows.append(f"
{''.join(row_cells)}
")
-
- # 生成表头
- stage_headers = "".join([f"
{stage} | " for stage in existing_basic_stages])
- action_headers = "".join(
- [f"
{action_type}
(执行) | " for action_type in all_action_types]
- )
- focus_chat_table_header = f"
| 聊天流 | {stage_headers}{action_headers}
|---|
"
- focus_chat_rows = focus_chat_table_header + "\n" + "\n".join(chat_rows)
-
- # 全局阶段时间统计
- focus_stage_rows = "\n".join(
- [
- f"
| {stage} | {avg_time:.3f}秒 |
"
- for stage, avg_time in sorted(stat_data[FOCUS_AVG_TIMES_BY_STAGE].items())
- ]
- )
-
- # 聊天流Action选择比例对比表（横向表格）
- focus_chat_action_ratios_rows = ""
- if stat_data.get("focus_action_ratios_by_chat"):
- # 获取所有action类型（按全局频率排序）
- all_action_types_for_ratio = sorted(
- stat_data[FOCUS_ACTION_RATIOS].keys(), key=lambda x: stat_data[FOCUS_ACTION_RATIOS][x], reverse=True
- )
-
- if all_action_types_for_ratio:
- # 为每个聊天流生成数据行（按循环数排序）
- chat_ratio_rows = []
- for chat_id in sorted(
- stat_data[FOCUS_CYCLE_CNT_BY_CHAT].keys(),
- key=lambda x: stat_data[FOCUS_CYCLE_CNT_BY_CHAT][x],
- reverse=True,
- ):
- chat_name = self.name_mapping.get(chat_id, (chat_id, 0))[0]
- total_cycles = stat_data[FOCUS_CYCLE_CNT_BY_CHAT][chat_id]
- chat_action_counts = stat_data["focus_action_ratios_by_chat"].get(chat_id, {})
-
- row_cells = [f"
{chat_name}
({total_cycles}次循环) | "]
-
- # 添加每个action类型的数量和百分比
- for action_type in all_action_types_for_ratio:
- count = chat_action_counts.get(action_type, 0)
- ratio = (count / total_cycles * 100) if total_cycles > 0 else 0
- if count > 0:
- row_cells.append(f"
{count}
({ratio:.1f}%) | ")
- else:
- row_cells.append("
-
(0%) | ")
-
- chat_ratio_rows.append(f"
{''.join(row_cells)}
")
-
- # 生成表头
- action_headers = "".join([f"
{action_type} | " for action_type in all_action_types_for_ratio])
- chat_action_ratio_table_header = f"
| 聊天流 | {action_headers}
|---|
"
- focus_chat_action_ratios_rows = chat_action_ratio_table_header + "\n" + "\n".join(chat_ratio_rows)
-
- # 按Action类型的阶段时间统计（横向表格）
- focus_action_stage_rows = ""
- if stat_data[FOCUS_AVG_TIMES_BY_ACTION]:
- # 获取所有阶段（按固定顺序）
- stage_order = ["观察", "并行调整动作、处理", "规划器", "执行动作"]
- all_stages = []
- for stage in stage_order:
- if any(stage in stage_times for stage_times in stat_data[FOCUS_AVG_TIMES_BY_ACTION].values()):
- all_stages.append(stage)
-
- # 为每个Action类型生成一行
- action_rows = []
- for action_type in sorted(stat_data[FOCUS_AVG_TIMES_BY_ACTION].keys()):
- stage_times = stat_data[FOCUS_AVG_TIMES_BY_ACTION][action_type]
- row_cells = [f"
{action_type} | "]
-
- for stage in all_stages:
- time_val = stage_times.get(stage, 0.0)
- row_cells.append(f"
{time_val:.3f}秒 | ")
-
- action_rows.append(f"
{''.join(row_cells)}
")
-
- # 生成表头
- stage_headers = "".join([f"
{stage} | " for stage in all_stages])
- focus_action_stage_table_header = f"
| Action类型 | {stage_headers}
|---|
"
- focus_action_stage_rows = focus_action_stage_table_header + "\n" + "\n".join(action_rows)
-
- # 计算时间范围
- if period_name == "all_time":
- from src.manager.local_store_manager import local_storage
-
- start_time = datetime.fromtimestamp(local_storage["deploy_time"])
- time_range = (
- f"{start_time.strftime('%Y-%m-%d %H:%M:%S')} ~ {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
- )
- else:
- start_time = datetime.now() - period_delta
- time_range = (
- f"{start_time.strftime('%Y-%m-%d %H:%M:%S')} ~ {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
- )
-
- # 生成该时间段的Focus统计HTML
- section_html = f"""
-
-
{period_desc}Focus统计
-
统计时段: {time_range}
-
总循环数: {stat_data.get(FOCUS_TOTAL_CYCLES, 0)}
-
-
-
-
全局阶段平均时间
-
- | 阶段 | 平均时间 |
|---|
- {focus_stage_rows}
-
-
-
-
-
Action类型分布
-
- | Action类型 | 次数 | 占比 |
|---|
- {focus_action_rows}
-
-
-
-
-
-
-
-
聊天流Action选择比例对比
-
-
- {focus_chat_action_ratios_rows}
-
-
-
-
-
Action类型阶段时间详情
-
-
- {focus_action_stage_rows}
-
-
-
- """
-
- focus_sections.append(section_html)
-
- # 如果没有任何Focus数据
- if not focus_sections:
- focus_sections.append("""
-
-
暂无Focus统计数据
-
在指定时间段内未找到任何Focus循环数据。
-
请确保 log/hfc_loop/ 目录下存在相应的JSON文件。
-
- """)
-
- return f"""
-
-
Focus系统详细统计
-
- 数据来源: log/hfc_loop/ 目录下的JSON文件
- 统计内容: 各时间段的Focus循环性能分析
-
-
- {"".join(focus_sections)}
-
-
-
- """
-
- def _generate_versions_tab(self, stat: dict[str, Any]) -> str:
- """生成版本对比独立分页的HTML内容"""
-
- # 为每个时间段准备版本对比数据
- version_sections = []
-
- for period_name, period_delta, period_desc in self.stat_period:
- stat_data = stat.get(period_name, {})
-
- if not stat_data.get(FOCUS_CYCLE_CNT_BY_VERSION):
- continue
-
- # 获取所有版本（按循环数排序）
- all_versions = sorted(
- stat_data[FOCUS_CYCLE_CNT_BY_VERSION].keys(),
- key=lambda x: stat_data[FOCUS_CYCLE_CNT_BY_VERSION][x],
- reverse=True,
- )
-
- # 生成版本Action分布表
- focus_version_action_rows = ""
- if stat_data[FOCUS_ACTION_RATIOS_BY_VERSION]:
- # 获取所有action类型
- all_action_types_for_version = set()
- for version_actions in stat_data[FOCUS_ACTION_RATIOS_BY_VERSION].values():
- all_action_types_for_version.update(version_actions.keys())
- all_action_types_for_version = sorted(all_action_types_for_version)
-
- if all_action_types_for_version:
- version_action_rows = []
- for version in all_versions:
- version_actions = stat_data[FOCUS_ACTION_RATIOS_BY_VERSION].get(version, {})
- total_cycles = stat_data[FOCUS_CYCLE_CNT_BY_VERSION][version]
-
- row_cells = [f"
{version}
({total_cycles}次循环) | "]
-
- for action_type in all_action_types_for_version:
- count = version_actions.get(action_type, 0)
- ratio = (count / total_cycles * 100) if total_cycles > 0 else 0
- row_cells.append(f"
{count}
({ratio:.1f}%) | ")
-
- version_action_rows.append(f"
{''.join(row_cells)}
")
-
- # 生成表头
- action_headers = "".join(
- [f"
{action_type} | " for action_type in all_action_types_for_version]
- )
- version_action_table_header = f"
| 版本 | {action_headers}
|---|
"
- focus_version_action_rows = version_action_table_header + "\n" + "\n".join(version_action_rows)
-
- # 生成版本阶段时间表（按action类型分解执行时间）
- focus_version_stage_rows = ""
- if stat_data[FOCUS_AVG_TIMES_BY_VERSION]:
- # 基础三个阶段
- basic_stages = ["观察", "并行调整动作、处理", "规划器"]
-
- # 获取所有action类型用于执行时间列
- all_action_types_for_exec = set()
- if stat_data.get("focus_exec_times_by_version_action"):
- for version_actions in stat_data["focus_exec_times_by_version_action"].values():
- all_action_types_for_exec.update(version_actions.keys())
- all_action_types_for_exec = sorted(all_action_types_for_exec)
-
- # 检查哪些基础阶段存在数据
- existing_basic_stages = []
- for stage in basic_stages:
- stage_exists = False
- for version_stages in stat_data[FOCUS_AVG_TIMES_BY_VERSION].values():
- if stage in version_stages:
- stage_exists = True
- break
- if stage_exists:
- existing_basic_stages.append(stage)
-
- # 构建表格
- if existing_basic_stages or all_action_types_for_exec:
- version_stage_rows = []
-
- # 为每个版本生成数据行
- for version in all_versions:
- version_stages = stat_data[FOCUS_AVG_TIMES_BY_VERSION].get(version, {})
- total_cycles = stat_data[FOCUS_CYCLE_CNT_BY_VERSION][version]
-
- row_cells = [f"
{version}
({total_cycles}次循环) | "]
-
- # 添加基础阶段时间
- for stage in existing_basic_stages:
- time_val = version_stages.get(stage, 0.0)
- row_cells.append(f"
{time_val:.3f}秒 | ")
-
- # 添加不同action类型的执行时间
- for action_type in all_action_types_for_exec:
- # 获取该版本该action类型的平均执行时间
- version_exec_times = stat_data.get("focus_exec_times_by_version_action", {})
- if version in version_exec_times and action_type in version_exec_times[version]:
- exec_time = version_exec_times[version][action_type]
- row_cells.append(f"
{exec_time:.3f}秒 | ")
- else:
- row_cells.append("
- | ")
-
- version_stage_rows.append(f"
{''.join(row_cells)}
")
-
- # 生成表头
- basic_headers = "".join([f"
{stage} | " for stage in existing_basic_stages])
- action_headers = "".join(
- [
- f"
执行时间
[{action_type}] | "
- for action_type in all_action_types_for_exec
- ]
- )
- version_stage_table_header = f"
| 版本 | {basic_headers}{action_headers}
|---|
"
- focus_version_stage_rows = version_stage_table_header + "\n" + "\n".join(version_stage_rows)
-
- # 计算时间范围
- if period_name == "all_time":
- from src.manager.local_store_manager import local_storage
-
- start_time = datetime.fromtimestamp(local_storage["deploy_time"])
- time_range = (
- f"{start_time.strftime('%Y-%m-%d %H:%M:%S')} ~ {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
- )
- else:
- start_time = datetime.now() - period_delta
- time_range = (
- f"{start_time.strftime('%Y-%m-%d %H:%M:%S')} ~ {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
- )
-
- # 生成该时间段的版本对比HTML
- section_html = f"""
-
-
{period_desc}版本对比
-
统计时段: {time_range}
-
包含版本: {len(all_versions)} 个版本
-
-
-
-
版本Action类型分布对比
-
-
- {focus_version_action_rows}
-
-
-
-
-
版本阶段时间对比
-
-
- {focus_version_stage_rows}
-
-
-
-
- """
-
- version_sections.append(section_html)
-
- # 如果没有任何版本数据
- if not version_sections:
- version_sections.append("""
-
-
暂无版本对比数据
-
在指定时间段内未找到任何版本信息。
-
请确保 log/hfc_loop/ 目录下的JSON文件包含版本信息。
-
- """)
-
- return f"""
-
-
Focus HFC版本对比分析
-
- 对比内容: 不同版本的Action类型分布和各阶段性能表现
- 数据来源: log/hfc_loop/ 目录下JSON文件中的version字段
-
-
- {"".join(version_sections)}
-
-
-
- """
-
def _generate_chart_data(self, stat: dict[str, Any]) -> dict:
"""生成图表数据"""
now = datetime.now()
@@ -1865,7 +959,7 @@ class StatisticOutputTask(AsyncTask):
# 查询LLM使用记录
query_start_time = start_time
- for record in LLMUsage.select().where(LLMUsage.timestamp >= query_start_time):
+ for record in LLMUsage.select().where(LLMUsage.timestamp >= query_start_time): # type: ignore
record_time = record.timestamp
# 找到对应的时间间隔索引
@@ -1875,7 +969,7 @@ class StatisticOutputTask(AsyncTask):
if 0 <= interval_index < len(time_points):
# 累加总花费数据
cost = record.cost or 0.0
- total_cost_data[interval_index] += cost
+ total_cost_data[interval_index] += cost # type: ignore
# 累加按模型分类的花费
model_name = record.model_name or "unknown"
@@ -1892,7 +986,7 @@ class StatisticOutputTask(AsyncTask):
# 查询消息记录
query_start_timestamp = start_time.timestamp()
- for message in Messages.select().where(Messages.time >= query_start_timestamp):
+ for message in Messages.select().where(Messages.time >= query_start_timestamp): # type: ignore
message_time_ts = message.time
# 找到对应的时间间隔索引
@@ -1917,71 +1011,16 @@ class StatisticOutputTask(AsyncTask):
message_by_chat[chat_name] = [0] * len(time_points)
message_by_chat[chat_name][interval_index] += 1
- # 查询Focus循环记录
- focus_cycles_by_action = {}
- focus_time_by_stage = {}
-
- log_dir = "log/hfc_loop"
- if os.path.exists(log_dir):
- json_files = glob.glob(os.path.join(log_dir, "*.json"))
- for json_file in json_files:
- try:
- # 解析文件时间
- filename = os.path.basename(json_file)
- name_parts = filename.replace(".json", "").split("_")
- if len(name_parts) >= 4:
- date_str = name_parts[-2]
- time_str = name_parts[-1]
- file_time_str = f"{date_str}_{time_str}"
- file_time = datetime.strptime(file_time_str, "%Y%m%d_%H%M%S")
-
- if file_time >= start_time:
- with open(json_file, "r", encoding="utf-8") as f:
- cycles_data = json.load(f)
-
- for cycle in cycles_data:
- try:
- timestamp_str = cycle.get("timestamp", "")
- if timestamp_str:
- cycle_time = datetime.fromisoformat(timestamp_str.replace("Z", "+00:00"))
- else:
- cycle_time = file_time
-
- if cycle_time >= start_time:
- # 计算时间间隔索引
- time_diff = (cycle_time - start_time).total_seconds()
- interval_index = int(time_diff // interval_seconds)
-
- if 0 <= interval_index < len(time_points):
- action_type = cycle.get("action_type", "unknown")
- step_times = cycle.get("step_times", {})
-
- # 累计action类型数据
- if action_type not in focus_cycles_by_action:
- focus_cycles_by_action[action_type] = [0] * len(time_points)
- focus_cycles_by_action[action_type][interval_index] += 1
-
- # 累计阶段时间数据
- for stage, time_val in step_times.items():
- if stage not in focus_time_by_stage:
- focus_time_by_stage[stage] = [0] * len(time_points)
- focus_time_by_stage[stage][interval_index] += time_val
- except Exception:
- continue
- except Exception:
- continue
-
return {
"time_labels": time_labels,
"total_cost_data": total_cost_data,
"cost_by_model": cost_by_model,
"cost_by_module": cost_by_module,
"message_by_chat": message_by_chat,
- "focus_cycles_by_action": focus_cycles_by_action,
- "focus_time_by_stage": focus_time_by_stage,
}
def _generate_chart_tab(self, chart_data: dict) -> str:
+ # sourcery skip: extract-duplicate-method, move-assign-in-block
"""生成图表选项卡HTML内容"""
# 生成不同颜色的调色板
@@ -2069,14 +1108,8 @@ class StatisticOutputTask(AsyncTask):
-
-
-
-
-
-
-
+