From 1cbd38a8e07f0a742ff2de7c08403cd28fdc478e Mon Sep 17 00:00:00 2001 From: DXC Date: Mon, 8 Jun 2026 12:12:11 +0800 Subject: [PATCH] =?UTF-8?q?chore:=20=E4=BB=8E=E7=B4=A2=E5=BC=95=E7=A7=BB?= =?UTF-8?q?=E9=99=A4=E8=BF=90=E8=A1=8C=E6=97=B6=E4=BA=A7=E7=89=A9=E3=80=81?= =?UTF-8?q?=E4=B8=AA=E4=BA=BA=E9=85=8D=E7=BD=AE=E3=80=81=E6=97=A7=E8=84=9A?= =?UTF-8?q?=E6=89=8B=E6=9E=B6=EF=BC=9B=E5=AE=8C=E5=96=84=20.gitignore?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 38 + .qwen/settings.json | 25 - .qwen/settings.json.orig | 24 - .../code_replacement_state_audit/SKILL.md | 141 - .qwen/skills/facade_kwargs_defense/SKILL.md | 309 --- .qwen/skills/wq_gui_data_flow/SKILL.md | 206 -- .../wq_gui_external_model_panel/SKILL.md | 294 -- .../skills/wq_gui_frontend_scaffold/SKILL.md | 229 -- 1.py | 4 - data/sub/png/watermask.png | Bin 18802 -> 0 bytes data/sub/waterindex.csv | 46 - data/sub/waterindex.xlsx | Bin 13596 -> 0 bytes data/sub/waterindex1125.csv | 46 - new/app/api/_smoke_test_train.py | 201 -- new/app/api/endpoints.py | 222 -- new/app/api/modeling.py | 847 ------ new/app/core/algorithms/__init__.py | 40 - new/app/core/algorithms/base.py | 85 - new/app/core/algorithms/goodman.py | 123 - new/app/core/algorithms/kutser.py | 211 -- new/app/core/algorithms/registry.py | 135 - new/app/core/task_store.py | 91 - new/app/main.py | 64 - new/frontend/.gitignore | 24 - new/frontend/README.md | 5 - new/frontend/index.html | 13 - new/frontend/package-lock.json | 2412 ----------------- new/frontend/package.json | 27 - new/frontend/public/favicon.svg | 1 - new/frontend/public/icons.svg | 24 - new/frontend/src/App.vue | 225 -- new/frontend/src/api/request.ts | 15 - new/frontend/src/api/tasks.ts | 13 - new/frontend/src/assets/hero.png | Bin 13057 -> 0 bytes new/frontend/src/assets/vite.svg | 1 - new/frontend/src/assets/vue.svg | 1 - new/frontend/src/components/HelloWorld.vue | 95 - new/frontend/src/composables/useTaskPoller.ts | 51 - new/frontend/src/main.ts | 9 - new/frontend/src/style.css | 296 -- new/frontend/tsconfig.app.json | 14 - new/frontend/tsconfig.json | 7 - new/frontend/tsconfig.node.json | 24 - new/frontend/vite.config.ts | 7 - src/gui/crash_dump.txt | 103 - src/gui/scaler_params.pkl | Bin 1783 -> 0 bytes tset.py | 11 - 封装问题分析报告.md | 215 -- 软件说明.md | 2048 -------------- 软件说明2.md | 228 -- 降采样光谱.py | 121 - 51 files changed, 38 insertions(+), 9333 deletions(-) delete mode 100644 .qwen/settings.json delete mode 100644 .qwen/settings.json.orig delete mode 100644 .qwen/skills/code_replacement_state_audit/SKILL.md delete mode 100644 .qwen/skills/facade_kwargs_defense/SKILL.md delete mode 100644 .qwen/skills/wq_gui_data_flow/SKILL.md delete mode 100644 .qwen/skills/wq_gui_external_model_panel/SKILL.md delete mode 100644 .qwen/skills/wq_gui_frontend_scaffold/SKILL.md delete mode 100644 1.py delete mode 100644 data/sub/png/watermask.png delete mode 100644 data/sub/waterindex.csv delete mode 100644 data/sub/waterindex.xlsx delete mode 100644 data/sub/waterindex1125.csv delete mode 100644 new/app/api/_smoke_test_train.py delete mode 100644 new/app/api/endpoints.py delete mode 100644 new/app/api/modeling.py delete mode 100644 new/app/core/algorithms/__init__.py delete mode 100644 new/app/core/algorithms/base.py delete mode 100644 new/app/core/algorithms/goodman.py delete mode 100644 new/app/core/algorithms/kutser.py delete mode 100644 new/app/core/algorithms/registry.py delete mode 100644 new/app/core/task_store.py delete mode 100644 new/app/main.py delete mode 100644 new/frontend/.gitignore delete mode 100644 new/frontend/README.md delete mode 100644 new/frontend/index.html delete mode 100644 new/frontend/package-lock.json delete mode 100644 new/frontend/package.json delete mode 100644 new/frontend/public/favicon.svg delete mode 100644 new/frontend/public/icons.svg delete mode 100644 new/frontend/src/App.vue delete mode 100644 new/frontend/src/api/request.ts delete mode 100644 new/frontend/src/api/tasks.ts delete mode 100644 new/frontend/src/assets/hero.png delete mode 100644 new/frontend/src/assets/vite.svg delete mode 100644 new/frontend/src/assets/vue.svg delete mode 100644 new/frontend/src/components/HelloWorld.vue delete mode 100644 new/frontend/src/composables/useTaskPoller.ts delete mode 100644 new/frontend/src/main.ts delete mode 100644 new/frontend/src/style.css delete mode 100644 new/frontend/tsconfig.app.json delete mode 100644 new/frontend/tsconfig.json delete mode 100644 new/frontend/tsconfig.node.json delete mode 100644 new/frontend/vite.config.ts delete mode 100644 src/gui/crash_dump.txt delete mode 100644 src/gui/scaler_params.pkl delete mode 100644 tset.py delete mode 100644 封装问题分析报告.md delete mode 100644 软件说明.md delete mode 100644 软件说明2.md delete mode 100644 降采样光谱.py diff --git a/.gitignore b/.gitignore index 2e6be2a..d7fbf8d 100644 --- a/.gitignore +++ b/.gitignore @@ -155,3 +155,41 @@ tmp/ *.bak *.backup *~ + +# ============================================================ +# 不应进入版本控制的文件类型 +# ============================================================ + +# Qwen Code 用户配置（个人环境，每次 clone 都不同） +.qwen/settings.json +.qwen/settings.json.orig + +# Qwen Code 自动生成的 skill 文件（每次会话重新生成） +.qwen/skills/ + +# GUI 运行时生成的文件 +src/gui/scaler_params.pkl +src/gui/crash_dump.txt + +# 临时/调试脚本（根目录） +降采样光谱.py +1.py +tset.py + +# 报告与文档（本地工作产物） +封装问题分析报告.md +软件说明.md +软件说明2.md + +# 数据子目录中非 .gitkeep 的生成文件 +data/sub/waterindex*.csv +data/sub/waterindex*.xlsx +data/sub/png/watermask.png + +# 图标文件（仅需保留 vector/svg，删除像素图标压缩包副本） +data/icons-1/*.ico +data/icons/*.png +data/icons/word/*.png + +# 旧版脚手架（遗留实验代码） +new/ diff --git a/.qwen/settings.json b/.qwen/settings.json deleted file mode 100644 index 15395fd..0000000 --- a/.qwen/settings.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "permissions": { - "allow": [ - "Bash(\"c:\\users\\duxin\\appdata\\local\\programs\\python\\python311\\python.exe\" *)", - "Bash(get-childitem *)", - "Bash(select-object *)", - "Bash(python *)", - "Bash(where *)", - "Bash(conda *)", - "Bash(dir *)", - "Bash(cmd *)", - "Bash(del *)", - "Bash(powershell *)", - "Bash(git *)", - "Bash(type *)", - "Bash(.\\venv\\scripts\\python.exe *)", - "Bash(\"d:\\111\\office\\zhlduijie\\1.wq\\wq_gui\\venv\\scripts\\python.exe\" *)", - "Bash(c:\\users\\duxin\\appdata\\local\\programs\\python\\python311\\python.exe *)", - "Bash(venv\\scripts\\python.exe *)", - "Bash(findstr *)", - "Bash(select-string *)" - ] - }, - "$version": 4 -} \ No newline at end of file diff --git a/.qwen/settings.json.orig b/.qwen/settings.json.orig deleted file mode 100644 index 0fff81c..0000000 --- a/.qwen/settings.json.orig +++ /dev/null @@ -1,24 +0,0 @@ -{ - "permissions": { - "allow": [ - "Bash(\"c:\\users\\duxin\\appdata\\local\\programs\\python\\python311\\python.exe\" *)", - "Bash(get-childitem *)", - "Bash(select-object *)", - "Bash(python *)", - "Bash(where *)", - "Bash(conda *)", - "Bash(dir *)", - "Bash(cmd *)", - "Bash(del *)", - "Bash(powershell *)", - "Bash(git *)", - "Bash(type *)", - "Bash(.\\venv\\scripts\\python.exe *)", - "Bash(\"d:\\111\\office\\zhlduijie\\1.wq\\wq_gui\\venv\\scripts\\python.exe\" *)", - "Bash(c:\\users\\duxin\\appdata\\local\\programs\\python\\python311\\python.exe *)", - "Bash(venv\\scripts\\python.exe *)", - "Bash(findstr *)" - ] - }, - "$version": 4 -} \ No newline at end of file diff --git a/.qwen/skills/code_replacement_state_audit/SKILL.md b/.qwen/skills/code_replacement_state_audit/SKILL.md deleted file mode 100644 index 79659a7..0000000 --- a/.qwen/skills/code_replacement_state_audit/SKILL.md +++ /dev/null @@ -1,141 +0,0 @@ ---- -name: 代码替换请求的现状审计 -description: 处理用户"代码替换/新增"指令时，先审计磁盘真实状态再用 ask_user_question 确认——避免覆盖已落盘的高版本代码 -source: auto-skill -extracted_at: '2026-06-03T05:36:58.746Z' ---- - -# 代码替换请求的现状审计 - -## 适用场景 - -用户给出"代码替换"或"按某版本代码新增"指令，但**没有提供与磁盘当前状态对比信息**时。典型触发： - -- 用户贴了一段代码说"请帮我写/替换这个" -- 用户引用某个文档/旧版本/旧 chat 说"按这个来" -- 之前的 `state_snapshot` / `memory` / `git log` 描述可能与磁盘现状不一致 - -## 核心原则 - -**永远不要盲信"用户给的代码是最新版本"**——磁盘上的代码可能已经是更完善的版本（用户或其他 agent 已迭代过）。覆盖 = 丢功能。 - -直接覆盖的代价不一定是显式 bug，也可能是"丢失用户已批准的设计决策"（如 duck-type 探测 / ctx 抽象 / 信号协议 / 二次确认窗 / 错误定位）。 - -## 5 步标准操作 - -### 1. 确认文件存在 - -`glob` 或 `list_directory` 看目标文件是否已存在： - -- 不存在 → 新建 -- 存在 → 进入第 2 步审计 - -### 2. grep 关键符号 + 读关键段 - -- 找"用户贴的代码"里的 3-5 个关键符号（函数名 / 类名 / 关键常量 / import） -- 在磁盘文件里 grep 同样的符号 -- `read_file` 关键段（行号从 grep 结果直接拿） - -### 3. 构造差异对照表 - -列出： - -``` -| 目标文件 | 用户贴的版本 | 磁盘现有版本 | 直接覆盖会丢失 | -``` - -**关键列**："直接覆盖会丢失什么"——让用户判断成本。具体粒度到"功能模块 / 设计决策 / 防御层 / 入口协议"，不要写"代码差异"这种空话。 - -### 4. ask_user_question 让用户拍板 - -3 个标准选项（措辞可调，但**必须给出现状 + 三选一**）： - -- **A. 保留现状**（推荐，磁盘已是更新版）—— 直接进 Smoke Test -- **B. 强制覆盖到旧版** —— 写明丢什么 + 备份建议（git stash / 复制到 `_old.py`） -- **C. 混合：只取某段增量** —— 见第 5 步 - -**不要在第 1 次 ask 时就列具体的"哪段增量"**——先让用户在 A/B/C 之间选。如果选 C，再做第 5 步。 - -### 5. 若用户选 C，识别"真正增量" - -对比 1.0 vs 2.0，识别 1.0 真正独有的部分（2.0 没有的）： - -- ❌ 排除 1.0 比 2.0 简单的（2.0 是超集 / 工厂分层 / 多了 CLI） -- ❌ 排除 1.0 整体被 2.0 工厂分层超越的（_make_objective vs _build_model + _get_search_space） -- ✅ 关注 1.0 独有的功能层（即使 2.0 不"明显"需要） - -对每个候选增量，再问一次"采纳哪段"，让用户具体选（multiSelect=false，一次只选 1 段最稳）。 - -## 落地原则 - -执行"采纳 1.0 某段增量到 2.0"时： - -- **最小化外科手术式编辑**：只动需要动的文件，只改需要改的段 -- **保留 2.0 的设计决策**（duck-type 探测 / ctx 抽象 / 信号协议 / 二次确认窗 / 错误定位） -- **顶部 import 增量用 `replace_all=False` 单点插入**，避免破坏其他 import 顺序 -- **同名变量全链路替换**（如 `self.config` → `clean_config`）要贯穿 ctx 构造 / v2 调用 / v1 fallback，避免双源差异 -- **单步模式不一定要清洗**（不走 panel 完整 config，与清洗器无关） -- **清洗器这种"防患于未然"的代码要给日志**（`self.log_message.emit(f"[清洗器] 已删除 N 个未知 key")`）让运行时可见 - -## 验证三件套 - -落地后必跑： - -1. **AST 语法检查**：`ast.parse(open(p, encoding='utf-8-sig').read())` 对 5 个核心文件 - - 必加 `utf-8-sig`：WQ_GUI 的 water_quality_gui.py line 1 是 BOM，plain `utf-8` 必挂 -2. **关键符号 grep**：确认新代码的关键符号（import / 关键函数调用）都命中，hit 数符合预期 -3. **顶层导入测试**：用 mock PyQt5 + `sys.path.insert(0, 'src/gui/core')`，验证模块整体可加载 - - PyQt5 mock 模板见下方"参考代码" - - Windows 环境调 Python：用 conda env 的 `python.exe` 全路径，不要靠 PATH - -## 反例（不要做） - -- ❌ "按用户贴的代码原封不动写入"——1.0 简化版的覆盖陷阱 -- ❌ "保留 state_snapshot 描述"——state snapshot 可能不准确（写的是意图，磁盘才是事实） -- ❌ "用 git log 反推当前状态"——git log 不能反映工作区未提交改动 -- ❌ "靠 memory 推断当前状态"——memory 可能是 22 天前的（已确认过期） -- ❌ "磁盘和用户给的代码看起来一样就不审计"——一行之差可能就是"防弹层"丢失 - -## 参考代码 - -### PyQt5 mock 模板（worker_thread.py 顶层导入测试） - -```python -import os, sys -os.environ['GDAL_FILENAME_IS_UTF8'] = 'YES' -os.environ['SHAPE_ENCODING'] = 'UTF-8' -sys.path.insert(0, 'src/gui/core') - -import types -pyqt5 = types.ModuleType("PyQt5") -qtc = types.ModuleType("PyQt5.QtCore") -class _QThread: - def __init__(self, *a, **kw): pass -class _Signal: - def __init__(self, *a, **kw): pass -qtc.QThread = _QThread -qtc.pyqtSignal = _Signal -qtc.Qt = type("Qt", (), {"QueuedConnection": 1, "UserRole": 0})() -sys.modules["PyQt5"] = pyqt5 -sys.modules["PyQt5.QtCore"] = qtc - -import worker_thread -# 副作用: check_pipeline_dependencies() 会打印依赖检查日志（可忽略） -``` - -### Windows 上跑 conda env python - -```bat -cmd /c "D:\xxx\anconda\envs\XXX\python.exe D:\path\to\script.py" -``` - -PowerShell 单行 `python -c "..."` 在中文路径 / 双引号 / 单引号嵌套时易翻车，**写临时 .py 文件再用 `cmd /c` 调**最稳。 - -## 案例来源（2026-06-03 WQ_GUI 路线 B MVP） - -- 用户贴 1.0 简化版：300 行 automl_trainer / 简化 worker_thread.run() / 简化 on_run_all_clicked -- 磁盘上 2.0 落盘版：545 行 automl_trainer（_build_model + _get_search_space 工厂 / argparse CLI）/ duck-type 探测 v2 + PipelineContext 抽象 / 完整二次确认窗 / 失败步骤 _focus_step 定位 / [DEPRECATED] stop 保留 -- 1.0 唯一真增量 = **"防弹级参数清洗器"**（method_map 14 项 + inspect.signature 过滤未知 key + has_kwargs 豁免 + 未知 key 数量日志） -- 落地：worker_thread.py:run() 内 set_callback 之后插入 53 行清洗器，self.config 6 处替换为 clean_config -- 验证：5 文件 AST 全通过 + 关键符号 7 项命中 + PyQt5 mock 下 import 成功 -- 净增行数：407 → 457（+50 行） diff --git a/.qwen/skills/facade_kwargs_defense/SKILL.md b/.qwen/skills/facade_kwargs_defense/SKILL.md deleted file mode 100644 index 34f4ba5..0000000 --- a/.qwen/skills/facade_kwargs_defense/SKILL.md +++ /dev/null @@ -1,309 +0,0 @@ ---- -name: PipelineRunner Facade 防御性 kwargs 兜底 -description: WQ_GUI 14 个 stepX_... Facade 方法必须以 **kwargs 收尾——配合 PipelineRunner 调度模式杜绝 "unexpected keyword argument" TypeError -source: auto-skill -extracted_at: '2026-06-04T00:54:50.036Z' ---- - -# PipelineRunner Facade 防御性 kwargs 兜底 - -## 适用场景 - -在 WQ_GUI 中，**任何被 `PipelineRunner` 调用的 14 个 `stepX_...` Facade 方法**（位于 `src/core/water_quality_inversion_pipeline_GUI.py`），其形参表末尾**必须**带 `**kwargs`。触发信号： - -- 用户报错 `TypeError: stepX_xxx() got an unexpected keyword argument 'yyy'` -- 改 PIPELINE_STEPS 的 `requires` 列表 -- 新增 / 重命名一个 step 方法 -- 重构 PipelineRunner 的 `_invoke` 注入逻辑 - -## 核心原则 - -**Facade 的形参表 = 显式声明的形参 + `, **kwargs`**。`kwargs` 必须**严格位于形参表最后**（Python 语法硬要求）。 - -```python -# ✅ 正确 -def step3_remove_glint(self, img_path: str, - method: str = "subtract_nir", - # ... 30+ 业务形参 ... - skip_dependency_check: bool = False, - **kwargs) -> str: - ... - -# ❌ 错误：**kwargs 不能放中间或前面 -def step3_remove_glint(self, img_path, **kwargs, skip_dependency_check): # SyntaxError -``` - -## 为什么需要这层防御 - -`PipelineRunner._invoke`（`src/core/pipeline/runner.py`）会向方法注入两类参数： - -| 层 | 来源 | 形参 key 怎么定 | -|---|---|---| -| **L2** | ctx 字段（按 `spec.requires` 列表） | `_default_param_name(ctx_key)` 默认去 `_path` 后缀 | -| **L3** | `ctx.user_config[step_id]`（14 panel dict 整体） | dict 的 key 原样注入 | - -**L2 触发 TypeError 的真实场景**（2026-06-04 真实发生）： - -- `PIPELINE_STEPS.step3.requires = ["img_path", "water_mask_path", "glint_mask_path"]` -- Runner 注入 `kwargs["glint_mask_path"] = ctx.glint_mask_path` -- `step3_remove_glint` 形参表**没有** `glint_mask_path`（虽然业务上耀斑掩膜是在子调用 `GlintRemovalStep.run` 内部用的，Facade 本身不接） -- → **TypeError: step3_remove_glint() got an unexpected keyword argument 'glint_mask_path'** - -**L3 触发 TypeError 的场景**： - -- user_config 14 panel dict 里残留了旧字段名 / 跨 step 串味的字段 -- 任何 `user_config[step_id][k]` 中的 `k` 都会被注入 - -`**kwargs` 一次性解决两类问题。 - -## 14 个 Facade 方法清单（截至 2026-06-04 已全部带 **kwargs） - -| step | method | 形参表闭合示例 | -|---|---|---| -| 1 | `step1_generate_water_mask` | `output_path: Optional[str] = None, **kwargs) -> str:` | -| 2 | `step2_find_glint_area` | `skip_dependency_check: bool = False, **kwargs) -> str:` | -| 3 | `step3_remove_glint` | `skip_dependency_check: bool = False, **kwargs) -> str:` | -| 4 | `step4_process_csv` | `skip_dependency_check: bool = False, **kwargs) -> str:` | -| 5 | `step5_extract_training_spectra` | `skip_dependency_check: bool = False, **kwargs) -> str:` | -| 5.5 | `step5_5_calculate_water_quality_indices` | `skip_dependency_check: bool = False, **kwargs) -> str:` | -| 6 | `step6_train_models` | `skip_dependency_check: bool = False, **kwargs) -> str:` | -| 6.5 | `step6_5_non_empirical_modeling` | `skip_dependency_check: bool = False, **kwargs) -> Dict[str, str]:` | -| 6.75 | `step6_75_custom_regression` | `skip_dependency_check: bool = False, **kwargs) -> str:` | -| 7 | `step7_generate_sampling_points` | `skip_dependency_check: bool = False, **kwargs) -> str:` | -| 8 | `step8_predict_water_quality` | `skip_dependency_check: bool = False, **kwargs) -> Dict[str, str]:` | -| 8.5 | `step8_5_predict_with_non_empirical_models` | `skip_dependency_check: bool = False, **kwargs) -> Dict[str, str]:` | -| 8.75 | `step8_75_predict_with_custom_regression` | `skip_dependency_check: bool = False, **kwargs) -> Dict[str, str]:` | -| 9 | `step9_generate_distribution_map` | `skip_dependency_check: bool = False, **kwargs) -> str:` | - -## 标准操作 - -### 1. 编辑（最小外科手术式） - -每个方法的最后形参是 `skip_dependency_check: bool = False`，把这一行改成： - -```python - skip_dependency_check: bool = False, **kwargs) -> str: -``` - -**注意缩进必须与原行一致**（13 空格 / 35 空格 / 47 空格 / 48 空格不等，按方法原始缩进）。用 `edit` 工具的 old_string **必须含 docstring 第一行**（`"""步骤X: ..."""`）作唯一标识。 - -### 2. 验证 - -写一个临时校验脚本（项目根目录运行后删掉）： - -```python -import ast, re -target = r'D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\core\water_quality_inversion_pipeline_GUI.py' -src = open(target, encoding='utf-8-sig').read() -ast.parse(src) # AST 语法 - -expected = [ - 'step1_generate_water_mask', 'step2_find_glint_area', 'step3_remove_glint', - 'step4_process_csv', 'step5_extract_training_spectra', - 'step5_5_calculate_water_quality_indices', 'step6_train_models', - 'step7_generate_sampling_points', 'step8_predict_water_quality', - 'step9_generate_distribution_map', 'step6_5_non_empirical_modeling', - 'step6_75_custom_regression', 'step8_5_predict_with_non_empirical_models', - 'step8_75_predict_with_custom_regression', -] -pat = re.compile(r'def\s+(\w+)\s*\((?:[^()]|\([^()]*\))*\*\*kwargs\)\s*->\s*[^:]+:', re.DOTALL) -found = set(pat.findall(src)) -print('Missing:', [m for m in expected if m not in found]) -print('Extra :', [m for m in found if m not in expected]) -``` - -期望输出：两个列表都为空。 - -### 3. Windows 执行 - -```bat -cd /d D:\111\office\ZHLduijie\1.WQ\WQ_GUI -py _check.py -del _check.py -``` - -> 必加 `utf-8-sig`：`water_quality_inversion_pipeline_GUI.py` 头部可能含 BOM（`code_replacement_state_audit` skill 里有同样提示）。 - -## ⚠️ 这层防御不能解决什么 - -**` **kwargs` 兜底 ≠ 形参名错位修复**。如果 Runner 注入 `kwargs["training_csv_path"]` 而方法形参是 `csv_path`： - -- ✅ **不会报 TypeError**（`training_csv_path` 被 `**kwargs` 收走） -- ❌ **但 `csv_path` 仍是 None**（方法体内的 `if csv_path is not None: ... else: ...` 走 fallback 分支，可能读 `self.training_csv_path` 哨兵） - -**已知形参名错位的方法**（2026-06-04 已修，commit `64aa5b8`）： - -| step | Runner 注入的 ctx key | 方法实际形参 | 实际落地的修复 | -|---|---|---|---| -| step6_5 | `training_csv_path` | `csv_path` | `parameter_map={"training_csv_path": "csv_path"}` | -| step6_75 | （已切到 `indices_path`） | `csv_path` | ⚠️ 见下方"step6_75 路由修复"专题 | -| step8_5 | `models_dir` | `non_empirical_models_dir` | `parameter_map={"models_dir": "non_empirical_models_dir"}` | -| step8_75 | `models_dir` | `custom_regression_dir` | `parameter_map={"models_dir": "custom_regression_dir"}` | - -> **parameter_map** 是 `StepSpec` 已有字段（runner.py:33），作用是把 ctx 字段重命名到方法形参名。**优先用 parameter_map 而非改 requires**——保持 ctx 字段语义清晰（声明式描述上游依赖），形参名是方法私有约定。 - -### step6_75 路由修复（特殊案例，2026-06-04 commit `64aa5b8`） - -`step6_75_custom_regression` **不是简单的 ctx 字段名错位**——方法体内的 fallback 链透露了**真正的数据源是 `indices_path`**： - -```python -# step6_75 形参：csv_path -# 方法体 fallback: -# if csv_path is not None: input_csv = csv_path -# elif self.indices_path is not None: input_csv = self.indices_path # ★ 真相 -``` - -加 `parameter_map` 把 `training_csv_path → csv_path` 看似能跑通，但**实际用错了数据**（training_csv 是 step5 输出，不是 step6_75 想要的 indices CSV）。 - -**正确做法 = 同时改 requires + parameter_map**： - -```python -StepSpec( - step_id="step6_75", method_name="step6_75_custom_regression", - requires=["indices_path"], # ★ 从 training_csv_path 切到 indices_path - produces=["models_dir"], - parameter_map={"indices_path": "csv_path"}, # ★ 同步改 key - description="自定义回归分析", -), -``` - -**配合 `skip_when_missing` 兜底**：若用户没跑 step5_5（`ctx.indices_path` 为 None），runner 自动 skip 整个 step6_75，不会用错位数据静默执行。 - -**判别何时需要"路由切"vs"纯 rename"**： -- 看方法体 fallback 链：fallback 到 `self.indices_path`/`self.deglint_img_path`/其他 ctx 字段名 → **需要改 requires** -- 仅是 key 名字不同，方法体直接用形参 → 只改 parameter_map - -## L2 注入顺序冲突：多个 requires 字段解析到同一形参名 - -### 场景 - -`StepSpec.requires` 里有**多个 ctx 字段**，经过 `_default_param_name` / `parameter_map` 解析后，**会落到同一个方法形参名**。L2 注入是**顺序敏感**的（后者覆盖前者），后注入的会**默默覆盖**前一个的赋值。 - -### 真实案例（2026-06-04 step5 修复） - -业务需求：step5 真正需要 step4 产物 `processed_csv_path`，但**保留 raw `csv_path` 字段**作为 `user_config` 覆盖入口。 - -**❌ 错误的 parameter_map 写法**（用户原方案的隐藏 bug）： - -```python -StepSpec( - step_id="step5", method_name="step5_extract_training_spectra", - requires=["deglint_img_path", "processed_csv_path", "csv_path", ...], # raw csv_path 也在 - parameter_map={"processed_csv_path": "csv_path"}, # ★ 只映射了一个 - ... -) -``` - -L2 注入顺序（`runner.py:184-186`）： -1. `deglint_img_path` → `kwargs[deglint_img_path] = ctx.deglint_img_path` -2. `processed_csv_path` → `kwargs[csv_path] = ctx.processed_csv_path` ← 主路径生效 -3. `csv_path`（无映射 → 默认）→ `kwargs[csv_path] = ctx.csv_path` ← **后注入 None 覆盖了主路径！** - -**症状**：step5 形参 `csv_path` 拿到的是 raw `ctx.csv_path`（通常是 None），方法体 fallback 到 `self.processed_csv_path`——但这个 fallback 也可能是 None（step4 没跑），step5 内部空跑 → "**静默错误**"。 - -**✅ 修法：parameter_map 双向映射 + 占位名落 **kwargs**： - -```python -parameter_map={ - "processed_csv_path": "csv_path", # 主路径（注入到方法形参） - "csv_path": "_raw_csv_ignored", # 占位（落到 step5 形参列表末尾的 **kwargs） -}, -``` - -注入顺序重排后： -- `processed_csv_path` → `kwargs[csv_path] = ctx.processed_csv_path` ← 主路径 -- `csv_path` → `kwargs[_raw_csv_ignored] = ctx.csv_path` ← 落 **kwargs（被吞） - -step5 形参 `csv_path` 最终拿到 `ctx.processed_csv_path` 的值 ✓。 - -### 验证模板（行为模拟） - -写临时 `_verify_l2_inject.py` 复刻 `runner.py:184-186` 的 L2 注入循环，**不要只靠 AST 静态检查**——parameter_map 的 key 顺序、requires 的字段顺序都是动态的： - -```python -import sys -sys.path.insert(0, r'D:\111\office\ZHLduijie\1.WQ\WQ_GUI') -from src.core.pipeline.context import PipelineContext -from src.core.pipeline.runner import PIPELINE_STEPS - -spec5 = next(s for s in PIPELINE_STEPS if s.step_id == 'step5') - -# 复刻 L2 注入（与 runner.py:184-186 完全一致） -def l2_inject(spec, ctx): - kwargs = {} - for ctx_key in spec.requires: - param_name = spec.parameter_map.get(ctx_key, ctx_key) # ★ 必须原样复刻 - kwargs[param_name] = ctx.get(ctx_key) - return kwargs - -# 关键断言 -ctx = PipelineContext(processed_csv_path='/csv/processed.csv', csv_path='/csv/raw.csv') -kw = l2_inject(spec5, ctx) -assert kw.get('csv_path') == '/csv/processed.csv', \ - f"形参 csv_path 应等于 processed_csv_path, 实际 {kw.get('csv_path')!r}" -assert '_raw_csv_ignored' in kw, "占位名应被注入到 kwargs" -print(f'OK: csv_path 形参 = {kw["csv_path"]!r} (processed, 主路径正确)') -print(f'OK: _raw_csv_ignored 占位 = {kw["_raw_csv_ignored"]!r} (raw, 落 **kwargs 被吞)') -``` - -跑完删掉：`py _verify_l2_inject.py & del _verify_l2_inject.py`（Windows 一行模式）。 - -### 何时需要警惕这个冲突 - -修改 StepSpec 时检查清单（**先看这一段再写 parameter_map**）： - -- [ ] **requires 里是否有多于 1 个 ctx 字段，解析后会落到同名方法形参？** 典型撞车： - - 同名字段（如 `processed_csv_path` 和 `csv_path` 都能映射到 `csv_path`） - - 同名 `_default_param_name` 退化（如 `boundary_path` 和 `boundary_shp_path` 默认都映射到 `boundary_path`——但要注意 `_default_param_name` 已废弃去后缀，原样返回 ctx key，所以 `boundary_path` 和 `boundary_shp_path` 默认就会撞 `boundary_path` / `boundary_shp_path` 不会撞，要撞就必须显式 parameter_map） - - 字段名 + parameter_map 重命名撞车 -- [ ] **"主路径"字段在 requires 列表靠前位置**（让后续"备路径"覆盖，但**这不解决冲突**——只要有第二次注入就一定会覆盖） -- [ ] **"备路径"字段**用占位名 `_xxx_ignored` / `_xxx_kwargs_only` 映射，让它落到 **kwargs -- [ ] **确认方法形参表末尾有 `**kwargs`** 兜底（`facade_kwargs_defense` skill 核心要求，已 14/14 落地） - -### 反例（不要做） - -- ❌ "我让 `csv_path` 不在 requires 里就行了"——会**丢失 user_config 覆盖入口**（如果用户想用 raw CSV 而不是 processed） -- ❌ "改 L2 注入循环，让 parameter_map 字段最后注入"——会**改变 runner 通用语义**，影响所有 step 的注入顺序 -- ❌ "加 `if param_name in kwargs: continue` 在 L2 注入里"——隐式"第一次优先"语义，新人读代码摸不着头脑 -- ❌ "用 position in requires 做加权"——把数据语义（哪个字段优先级高）塞到列表顺序里，runner 应该保持"声明式" - -### 与"纯 rename"的区别 - -| 维度 | 纯 rename（已有 skill 案例） | 多→1 冲突（本节案例） | -|---|---|---| -| 典型场景 | step6_5/6_75/8_5/8_75：1 个 requires 重命名到形参 | step5：2 个 requires 撞到同一形参 | -| parameter_map | 1 个 key→value | 2 个 key→同名 value + 占位名 | -| requires | 1 个字段 | 2 个字段（主 + 备） | -| 冲突来源 | 不会出现（单 key） | 出现（顺序敏感 + 撞名） | -| 修法 | 只加 parameter_map | 双向 parameter_map + 占位名 | - -## 与其他防御层的关系 - -``` -PipelineRunner.run() 主循环 - ├─ L1 runner.py:152 skip_when_missing ─── ctx. 全 None → skip step - ├─ L2 runner.py:182 ctx 字段注入 ─── 形参表里没声明 → TypeError ⚠️ → **kwargs 兜底 - ├─ L3 runner.py:188 user_config 合并 ─── user_config 有"空字符串"/None → 跳过（上一轮加的守卫）✅ - └─ L4 runner.py:211 except 捕获 ─── 业务抛异常 → ctx.status="error" + raise -``` - -`**kwargs` 是 **L2 的"消极兜底"**——宁愿吞掉多余 key 也不报 TypeError。**真正的"积极修复"是 parameter_map**（让 ctx 字段名映射到正确形参名）。两层配合： -- **保守期间（重构初期）**：先 `**kwargs` 兜住，TypeError 消失 -- **稳定阶段**：补 parameter_map，让方法收到正确数据 - -## 反例（不要做） - -- ❌ "不写 `**kwargs`，靠 type hint + IDE 检查兜底"——Runner 是运行时注入，IDE 看不到 -- ❌ "把 `**kwargs` 放形参表中间"——Python 语法错误 -- ❌ "改 requires 列表去掉冗余 ctx 字段"——会导致 `skip_when_missing` 误判（以为 step 不需要该 ctx 字段），应该用 `parameter_map` 重命名而非删除 requires -- ❌ "在 14 个 Facade 方法体里加 `if 'glint_mask_path' in kwargs: kwargs.pop('glint_mask_path')`"——脏活，且每个方法都要加，远不如 `**kwargs` 一行优雅 - -## 案例来源 - -- 2026-06-04 WQ_GUI PipelineRunner 迁移第二步 -- 触发：`step3_remove_glint() got an unexpected keyword argument 'glint_mask_path'` -- 根因：`PIPELINE_STEPS.step3.requires` 写了 `glint_mask_path`，但 `GlintRemovalStep` 内部使用，Facade 自身不接这个形参 -- 落地：14 个 Facade 全部加 `, **kwargs`，0 个 TypeError -- 验证：临时 `_check.py` 14/14 命中 + AST 解析通过 -- 续：4 个 parameter_map 全部落地（commit `64aa5b8`），含 step6_75 路由切到 indices_path；L3 非空过滤同步加入 `runner._invoke:188` -- 2026-06-04 step5 严格依赖修复：发现 L2 注入顺序冲突（requires 多个字段解析到同一形参名），引入"双向 parameter_map + 占位名落 **kwargs"模式；step5 形参 `csv_path` 真正接到 `processed_csv_path`（step4 产物），raw `csv_path` 保留为 user_config 覆盖入口，落占位名 `_raw_csv_ignored` 后被 `**kwargs` 吞。skip_when_missing 块同步加 `_notify` 通知，**拒绝静默跳过**（15 条 _notify 全带具体 missing 字段列表证据）。 diff --git a/.qwen/skills/wq_gui_data_flow/SKILL.md b/.qwen/skills/wq_gui_data_flow/SKILL.md deleted file mode 100644 index a3a4964..0000000 --- a/.qwen/skills/wq_gui_data_flow/SKILL.md +++ /dev/null @@ -1,206 +0,0 @@ ---- -name: WQ_GUI 数据流转架构 -description: WQ_GUI ProjectSession 事件总线驱动的步骤间数据传递机制（完整重构版） -source: auto-skill -extracted_at: '2026-05-28T09:07:34.967Z' ---- - -# WQ_GUI 数据流转架构 - -## 核心结论 - -整个系统是**基于文件路径驱动**的管道，所有数据存储在本地磁盘。重构后通过 `ProjectSession` 事件总线实现 Panel 间完全解耦。 - ---- - -## 1. 旧架构（旧代码中已删除） - -主窗口通过 `self.step_outputs` 字典 + `step_dependencies` 配置 + `auto_populate_*` 系列方法管理步骤间路径填充。存在高度耦合问题： - -```python -# 已废弃并删除 -self.step_outputs = {} -self._init_step_dependencies() -self.update_step_outputs(step_name, work_path) -self.auto_populate_dependent_steps(completed_step) -self.auto_populate_step_inputs(step_id) -self.find_step_output(work_path, step_id, output_type) -self.add_auto_fill_buttons_to_panels() -self.scan_work_directory_for_files(work_path) -``` - ---- - -## 2. 新架构：ProjectSession 事件总线 - -### Session 核心 API（`src/core/project_session.py`） - -```python -class ProjectSession(QObject): - path_updated = pyqtSignal(str, str, str) # step, out_type, path - step_outputs_ready = pyqtSignal(str, str) # step, out_type - - def update_output(step, out_type, path): - """Panel 完成后广播输出路径""" - - def update_outputs(step, {out_type: path, ...}): - """Panel 完成后批量广播多个输出路径""" - - def get_output(step, out_type): - """Panel 可主动查询上游路径（用于自动填充）""" - - def get_step_outputs(step): - """返回该 step 的全部输出字典""" - - def scan_work_directory(): - """主窗口 on_step_completed 末尾调用，扫描并广播所有已知路径""" -``` - -### Panel 重构模板 - -```python -class StepXPanel(QWidget): - def __init__(self, session=None, parent=None): - super().__init__(parent) - self.session = session - self.work_dir = None - self.init_ui() - self._bind_session_signals() - - def _bind_session_signals(self): - if not self.session: - return - self.session.path_updated.connect( - self._on_session_path_updated, Qt.QueuedConnection - ) - - @pyqtSlot(str, str, str) - def _on_session_path_updated(self, step_name, output_type, path): - print(f"[StepX Debug] 收到广播: step={step_name}, type={output_type}, path={path}") - if step_name == 'step1': - if output_type == 'reference_img': - if not self.img_file.get_path().strip(): - self.img_file.set_path(path) - print(f"[StepX] 自动填充参考影像: {path}") - elif output_type == 'water_mask': - if not self.water_mask_file.get_path().strip(): - self.water_mask_file.set_path(path) - print(f"[StepX] 自动填充水域掩膜: {path}") - # ... - - def on_step_finished(self, success, message): - """由主窗口 on_step_completed 通过 getattr 动态调用""" - if not success: - return - if self.session: - outputs = {} - path = self.output_widget.get_path().strip() - if path: - outputs['output_type'] = path - if outputs: - self.session.update_outputs('stepX', outputs) -``` - -### 主窗口两处改动 - -```python -# 1. __init__ 中注入 session（所有 Panel 统一注入） -self.step1_panel = Step1Panel(session=self.session) -self.step2_panel = Step2Panel(session=self.session) -self.step3_panel = Step3Panel(session=self.session) -self.step4_panel = Step4Panel(session=self.session) -self.step5_panel = Step5Panel(session=self.session) -self.step5_5_panel = Step5_5Panel(session=self.session) -self.step6_panel = Step6Panel(session=self.session) -self.step6_5_panel = Step6_5Panel(session=self.session) -self.step6_75_panel = Step6_75Panel(session=self.session) -self.step7_panel = Step7Panel(session=self.session) -self.step8_panel = Step8Panel(session=self.session) -self.step8_5_panel = Step8_5Panel(session=self.session) -self.step8_75_panel = Step8_75Panel(session=self.session) -self.step9_panel = Step9Panel(session=self.session) - -# 2. on_step_completed（通用动态获取，无需维护字典） -def on_step_completed(self, step_name, success, message): - if not success: - return - if hasattr(self, 'session') and self.session: - self.session.scan_work_directory() - - panel = getattr(self, f"{step_name}_panel", None) - if panel and hasattr(panel, 'on_step_finished'): - panel.on_step_finished(success, message) -``` - ---- - -## 3. 全链路事件流 - -### step1 → step2 / step3 路径（通过 Shapefile 栅格化产物） - -| 场景 | 广播的 water_mask 路径 | -|------|----------------------| -| NDWI 模式 | `output_file` 用户指定路径 | -| Shapefile 模式 | `{work_dir}/1_water_mask/water_mask_from_shp.dat`（优先）
若文件不存在则 fallback 回 `mask_file.get_path()` | - -``` -step1 完成 - → step1_panel.on_step_finished() - → session.update_outputs('step1', { - 'reference_img': img_path, - 'water_mask': mask_path # 可能是 .dat 或 .shp（见上表） - }) - → step2_panel._on_session_path_updated() - → step3_panel._on_session_path_updated() -``` - -### step3 → step5 / step7；step5 → 下游训练 - -``` -step3.deglint_image ──┬─→ step5.deglint_image（填充 img_file） - └─→ step7.deglint_image（填充 img_file） - -step5.training_spectra ──┬─→ step5_5.index_features - ├─→ step6.models_dir ──→ step8.predictions - ├─→ step6_5.models_dir ──→ step8_5.predictions - └─→ step6_75.models_dir ──→ step8_75.predictions - -step7.sampling_points ──┬─→ step8 - ├─→ step8_5 - └─→ step8_75 - -step8/8_5/8_75.predictions ──→ step9.distribution_map -``` - -### 各 Panel 监听/发布对照表（完整版） - -| Panel | 监听 | 发布 | -|-------|------|------| -| step1 | — | `reference_img`, `water_mask` | -| step2 | `step1.reference_img`, `step1.water_mask` | `glint_mask` | -| step3 | `step1.reference_img`, `step1.water_mask`, `step2.glint_mask` | `deglint_image` | -| step4 | — | `processed_data` | -| step5 | `step3.deglint_image`, `step4.processed_data`, `step2.glint_mask` | `training_spectra` | -| step5_5 | `step5.training_spectra` | `index_features` | -| step6 | `step5.training_spectra` | `models_dir` | -| step6_5 | `step5.training_spectra` | `models_dir` | -| step6_75 | `step5.training_spectra` | `models_dir` | -| step7 | `step3.deglint_image`, `step1.water_mask`, `step2.glint_mask` | `sampling_points` | -| step8 | `step7.sampling_points`, `step6.models_dir` | `predictions` | -| step8_5 | `step7.sampling_points`, `step6_5.models_dir` | `predictions` | -| step8_75 | `step7.sampling_points`, `step6_75.models_dir` | `predictions` | -| step9 | `step8.predictions`, `step8_5.predictions`, `step8_75.predictions` | `distribution_map` | - ---- - -## 4. 关键约束 - -- `__init__` 参数 `session=None`（向后兼容，主窗口可继续不传） -- 所有 Panel 的 `init_ui / get_config / set_config / update_from_config` 完整保留 -- 删除所有 `self.window().stepX_panel` 跨界访问 -- 使用 `self.session.get_output()` 替代直接读取其他 panel 的 widget -- 监听使用 `Qt.QueuedConnection` 确保跨线程安全 -- 仅在 field 为空时自动填充（`not widget.get_path().strip()`） -- `update_from_config` 中优先从 Session 获取路径，再用 Session 广播 -- 主窗口 `on_step_completed` 中使用 `getattr(self, f"{step_name}_panel", None)` 实现通用动态获取，无需维护硬编码字典 -- `step1` Shapefile 模式下，**不能**直接广播 `.shp` 输入文件，必须拼接 `{work_dir}/1_water_mask/water_mask_from_shp.dat` 作为产物路径 \ No newline at end of file diff --git a/.qwen/skills/wq_gui_external_model_panel/SKILL.md b/.qwen/skills/wq_gui_external_model_panel/SKILL.md deleted file mode 100644 index 4a53d35..0000000 --- a/.qwen/skills/wq_gui_external_model_panel/SKILL.md +++ /dev/null @@ -1,294 +0,0 @@ ---- -name: WQ_GUI PyQt5 面板外部模型导入模式 -description: 在 Step8 等预测面板中通过 QRadioButton + FileSelectWidget + joblib.load 防御性加载实现"内置/导入"双模式切换的标准模式 -source: auto-skill -extracted_at: '2026-06-08T01:38:14.481Z' ---- - -# WQ_GUI PyQt5 面板外部模型导入模式 - -## 适用场景 - -Step8（机器学习预测）、Step8_5、Step8_75 等面板需要同时支持： -1. **内置模式**：使用 `step6` 训练流程生成的模型目录 -2. **导入模式**：用户手动选择本地预训练 `.joblib` 文件直接加载 - ---- - -## 1. 模板（可直接复制到 `__init__` + `init_ui`） - -```python -from PyQt5.QtWidgets import QRadioButton - -class StepXPanel(QWidget): - def __init__(self, parent=None): - super().__init__(parent) - self.current_model = None # ★ 外部模型实例缓存 - self.init_ui() - - def init_ui(self): - layout = QVBoxLayout() - - # -------- 模型来源选择（单选按钮组） -------- - source_group = QGroupBox("模型来源") - source_layout = QVBoxLayout() - - self.use_trained_model = QRadioButton("使用当前训练流程的模型") - self.use_external_model = QRadioButton("导入本地预训练模型 (.joblib)") - self.use_trained_model.setChecked(True) - source_layout.addWidget(self.use_trained_model) - source_layout.addWidget(self.use_external_model) - - self.use_trained_model.toggled.connect(self._on_model_source_changed) - self.use_external_model.toggled.connect(self._on_model_source_changed) - - source_group.setLayout(source_layout) - layout.addWidget(source_group) - - # -------- 外部模型文件选择（条件显示） -------- - self.external_model_widget = FileSelectWidget( - "预训练模型:", - "Joblib Files (*.joblib);;All Files (*.*)" - ) - # FileSelectWidget 的 browse_btn 默认连着 open file 行为， - # 需要先断开默认连接，再接自定义槽 - self.external_model_widget.browse_btn.clicked.disconnect() - self.external_model_widget.browse_btn.clicked.connect(self._browse_external_model) - self.external_model_widget.setVisible(False) - layout.addWidget(self.external_model_widget) - - # ... 其余原有 UI ... -``` - ---- - -## 2. 槽函数模板 - -### `_on_model_source_changed` - -单选按钮 `toggled` 信号在**两个**按钮上都会触发（点击 A 时 A 触发，B 也触发），所以用 `if not checked: return` 让非选中分支短路。 - -```python -def _on_model_source_changed(self, checked: bool): - """单选按钮切换：控制外部模型文件选择控件的显示/隐藏""" - if not checked: - return - is_external = self.use_external_model.isChecked() - self.external_model_widget.setVisible(is_external) - # 切回"使用当前模型"时清空缓存，释放内存并避免误用旧模型 - if not is_external: - self.current_model = None -``` - -### `_browse_external_model` - -- 用 `QFileDialog.getOpenFileName` 而非 `getExistingDirectory` -- 防御性解析两种格式：`{"model": pipeline, ...}`（Step6 输出格式）和裸 `Pipeline` 对象 -- 失败用 `QMessageBox.warning` 友善提示；成功用 `QMessageBox.information` 告知 - -```python -from PyQt5.QtWidgets import QFileDialog, QMessageBox -from pathlib import Path - -def _browse_external_model(self): - """浏览并加载外部 .joblib 预训练模型文件""" - default = self._get_default_work_dir() - path, _ = QFileDialog.getOpenFileName( - self, - "选择预训练模型 (.joblib)", - default, - "Joblib Files (*.joblib);;All Files (*.*)", - ) - if not path: - return - - try: - import joblib - loaded = joblib.load(path) - # 兼容两种格式：dict{"model": obj} 或裸 Pipeline - if isinstance(loaded, dict) and "model" in loaded: - self.current_model = loaded["model"] - elif hasattr(loaded, "predict"): - self.current_model = loaded - else: - QMessageBox.warning( - self, - "模型格式错误", - f"无法识别的模型格式，文件内容类型为：{type(loaded).__name__}", - ) - return - self.external_model_widget.set_path(path) - QMessageBox.information( - self, - "模型加载成功", - f"已加载模型：{Path(path).name}\n类型：{type(self.current_model).__name__}", - ) - except Exception as e: - self.current_model = None - QMessageBox.warning( - self, - "模型加载失败", - f"加载模型时发生错误：\n{type(e).__name__}: {e}", - ) -``` - ---- - -## 3. `run_step` 改造模板 - -在原有目录加载逻辑之前，插入外部模型优先分支： - -```python -def run_step(self): - """独立运行步骤X""" - # ... 公共输入校验 ... - - # ★ 外部模型优先分支 - if self.use_external_model.isChecked(): - if self.current_model is None: - QMessageBox.warning( - self, - "模型未加载", - "请先点击「浏览...」按钮加载预训练模型文件！", - ) - return - external_model_path = self.external_model_widget.get_path() or "" - main_window = self.window() - if hasattr(main_window, 'run_single_step'): - config = { - 'stepX': self.get_config(), - '_external_model': self.current_model, # ★ 直接传对象 - '_external_model_path': external_model_path, # 供日志/回溯用 - } - main_window.run_single_step('stepX', config) - return - - # 默认流程：使用模型目录（原有逻辑不变） - models_dir = self.models_dir_file.get_path() - if not models_dir: - QMessageBox.warning(self, "输入错误", "请选择模型目录！") - return - # ... 原有 run_step 剩余代码 ... -``` - ---- - -## 4. 后端三层完整接入（2026-06-08 已落地） - -完整数据流分为三层，每层各一处分流点： - -``` -GUI step8_panel - ↓ config = {'_external_model': obj, '_external_model_path': path, 'step8': {...}} - ↓ -worker_thread.run_single_step() [第1处分流：透传顶层 key] - ↓ step_config = config['step8'] + {'_external_model': obj, '_external_model_path': path} - ↓ -prediction_step.predict_water_quality() [第2处分流：接收 + 透传] - ↓ _external_model=obj, _external_model_path=path - ↓ -WaterQualityInference(artifacts_dir, external_model=obj, external_model_path=path) - ↓ -inference_batch.batch_inference_multi_models() [第3处分流：effective_model 短路] - ↓ external_model=obj - ↓ -inference_batch.inference_pipeline() - → self.external_model is not None → self.loaded_model_data = self.external_model（跳过磁盘加载） -``` - -### 4a. worker_thread.py — run_single_step 透传 - -在 `step_config = dict(config.get(step_name, {}))` 之后、"skip_dependency_check" 之前插入： - -```python -# 透传面板顶层传入的外部预训练模型（GUI step8_panel 通过 config['_external_model'] 传入） -# 非空才覆盖（遵循 feedback_never_overwrite_with_empty 原则） -for key in ('_external_model', '_external_model_path'): - val = config.get(key) - if val is not None and val != "": - step_config[key] = val -``` - -### 4b. prediction_step.py — predict_water_quality 签名 + 透传 - -形参表末尾增加两个参数： - -```python -_external_model=None, -_external_model_path=None, -``` - -构造处透传： - -```python -inferencer = WaterQualityInference( - models_dir, - external_model=_external_model, - external_model_path=_external_model_path, -) -all_results = inferencer.batch_inference_multi_models( - models_root_dir=models_dir, - ... - external_model=_external_model, - external_model_path=_external_model_path, -) -``` - -### 4c. inference_batch.py — 三处修改 - -**① `__init__` 存储**： - -```python -def __init__(self, artifacts_dir: str = "models/artifacts", - external_model=None, external_model_path=None): - ... - self.external_model = external_model - self.external_model_path = external_model_path -``` - -**② `batch_inference_multi_models` 短路 + 注入**： - -```python -# 优先级：外部预训练模型 > 从磁盘加载 -if external_model is not None: - effective_model = external_model - print(f"\n使用外部预训练模型: type={type(external_model).__name__}") -else: - effective_model = None - -# 子目录循环中注入： -if effective_model is not None: - model_inferencer = WaterQualityInference( - str(subdir), - external_model=effective_model, - external_model_path=external_model_path, - ) -else: - model_inferencer = WaterQualityInference(str(subdir)) -``` - -**③ `inference_pipeline` 模型加载短路**（`load_best_model` 调用前）： - -```python -if self.external_model is not None: - self.loaded_model_data = self.external_model - print(f" 使用外部预训练模型: type={type(self.external_model).__name__}") -elif model_file_path: - self.load_specific_model(model_file_path) -else: - self.load_best_model(metric=metric) -``` - -**关键约束**： -- `joblib.load` 在 panel 槽函数里完成（GUI 进程内），对象通过 config 引用直接透传；**不跨进程**，所以不需要担心 pickle 序列化问题 -- `batch_inference_multi_models` 形参 `external_model` 和 `external_model_path` **与实例属性同名**（`self.external_model`），两者都传是为了让每个子目录创建的 `WaterQualityInference` 实例都能独立持有引用 -- 原有从 `models_dir` 目录加载的逻辑完全保留，只在 `external_model is not None` 时短路 - ---- - -## 5. 已知约束 - -- `FileSelectWidget.browse_btn.clicked` 在 `init_ui` 里会重复 connect，每次 `init_ui` 被调用时会累积；解决方案是在 connect 前先 `.disconnect()`（如模板所示）。 -- `QRadioButton.toggled` 信号在两个按钮上都会触发，**必须**用 `if not checked: return` 短路，否则会导致切换时状态错乱。 -- `self.current_model` 会在面板切换到"使用当前模型"时清空，防止用户忘记换回内置模式后仍使用旧导入模型。 -- 当前项目 venv 路径：`D:\111\office\ZHLduijie\1.WQ\WQ_GUI\venv`，导入 `joblib` 时注意 venv 环境一致性。 \ No newline at end of file diff --git a/.qwen/skills/wq_gui_frontend_scaffold/SKILL.md b/.qwen/skills/wq_gui_frontend_scaffold/SKILL.md deleted file mode 100644 index 5d26c65..0000000 --- a/.qwen/skills/wq_gui_frontend_scaffold/SKILL.md +++ /dev/null @@ -1,229 +0,0 @@ ---- -name: WQ_GUI 前端 Vue3 + Element Plus 脚手架 -description: WQ_GUI 项目 frontend/ 目录的 Vite + Vue 3 + TS + Element Plus 最小可运行脚手架，以及 useTaskPoller 与 Element Plus UI 的接线模式 -source: auto-skill -extracted_at: '2026-06-02T08:17:33.116Z' ---- - -# WQ_GUI 前端脚手架 (Vue 3 + Element Plus) - -## 适用场景 - -为 WQ_GUI FastAPI 后端 (`127.0.0.1:8000`) 搭建一个**最小可联调**的浏览器控制台。 -后端已暴露： - -- `POST /api/modeling/train` → `{ task_id, status, kind }` -- `POST /api/modeling/predict` → `{ task_id, status, kind }` -- `GET /api/tasks/{task_id}` → `TaskRecord`（含 PENDING/PROCESSING/SUCCESS/FAILED + 模型指标 / 输出路径） -- `GET /api/algorithms` → 算法清单 - -前端已有 (`frontend/src/`): - -- `api/request.ts`：axios 单例 + 响应拦截器自动 unwrap，baseURL 走 `VITE_API_BASE_URL` 缺省 `http://127.0.0.1:8000` -- `api/tasks.ts`：所有提交 / 查询函数 + 完整 `TaskRecord` / `TaskStatus` / `TaskKind` 类型 -- `composables/useTaskPoller.ts`：完整轮询 composable，支持 3 种用法（静态 / 响应式 taskId / 手动） - -## 1. 一次性补齐的脚手架文件 - -`frontend/` 初始状态**只有 `src/api` 和 `src/composables`**，缺整个 Vite 骨架。直接照下面这 7 个文件铺一遍： - -``` -frontend/ -├── .env.development # VITE_API_BASE_URL=http://127.0.0.1:8000 -├── .gitignore # node_modules / dist / .vite -├── env.d.ts # vite/client + ImportMeta + *.vue shim -├── index.html # 挂载 #app -├── package.json -├── tsconfig.json # 严格模式 + @ → src + bundler resolution -├── tsconfig.node.json # 给 vite.config.ts 用 -├── vite.config.ts # @ alias + 0.0.0.0:5173 -└── src/ - ├── main.ts - └── App.vue -``` - -### 锁定版本（2026-06 联调通过） - -```json -{ - "dependencies": { - "vue": "^3.4.27", - "element-plus": "^2.7.5", - "@element-plus/icons-vue": "^2.3.1", - "axios": "^1.7.2" - }, - "devDependencies": { - "@types/node": "^20.12.12", - "@vitejs/plugin-vue": "^5.0.4", - "typescript": "^5.4.5", - "vite": "^5.2.11", - "vue-tsc": "^2.0.19" - } -} -``` - -**`@types/node` 必加**——`vite.config.ts` 用了 `import { fileURLToPath, URL } from 'node:url'`，否则 `npm run build` 类型检查必挂。 - -### `tsconfig.json` 关键字段 - -- `"moduleResolution": "bundler"` -- `"allowImportingTsExtensions": true`（配合 `vue-tsc --noEmit`） -- `"paths": { "@/*": ["src/*"] }` + `"baseUrl": "."` -- `"include": ["src/**/*.vue"]`（`vue-tsc` 才会处理 SFC） -- `"references": [{ "path": "./tsconfig.node.json" }]` - -### `vite.config.ts` 关键字段 - -```ts -resolve: { - alias: { '@': fileURLToPath(new URL('./src', import.meta.url)) }, -}, -server: { host: '0.0.0.0', port: 5173 }, -``` - -`0.0.0.0` 方便局域网真机调试；端口冲突时 `strictPort: false` 允许 Vite 自动 +1。 - ---- - -## 2. main.ts 模板（全量注册 Element Plus） - -```ts -import { createApp } from 'vue' -import ElementPlus from 'element-plus' -import 'element-plus/dist/index.css' -import * as ElementPlusIconsVue from '@element-plus/icons-vue' - -import App from './App.vue' - -const app = createApp(App) -app.use(ElementPlus) - -// 全量注册图标 () -for (const [name, component] of Object.entries(ElementPlusIconsVue)) { - app.component(name, component) -} -app.mount('#app') -``` - -联调期**全量注册最省事**；后期打包体积大再换 `unplugin-vue-components` 按需。 - ---- - -## 3. useTaskPoller 接线模式（双实例） - -训练 / 推断是**两条独立流水线**，各起一个 `useTaskPoller` 实例。核心套路：把 `task_id` 包成 `ref(null)`，composable 内部 `watch` 会**自动 start()**，无需手动调： - -```ts -import { ref, watch, computed } from 'vue' -import { submitTrain, submitPredict, type TaskRecord } from './api/tasks' -import { useTaskPoller } from './composables/useTaskPoller' - -// —— 训练 —— -const trainTaskId = ref(null) -const trainPoller = useTaskPoller(trainTaskId) // 传 ref 进去, 自动 watch - -async function onStartTrain() { - const { task_id } = await submitTrain({ ... }) - trainTaskId.value = task_id // 赋值后 watch 触发 start() -} - -// —— 推断 —— -const predictTaskId = ref(null) -const predictPoller = useTaskPoller(predictTaskId) -const modelId = ref('') - -// 训练一成功, model_id 自动填入推断输入框 -watch( - () => trainPoller.result.value?.model_id, - (newId) => { if (newId) modelId.value = newId }, -) - -async function onStartPredict() { - const { task_id } = await submitPredict({ model_id: modelId.value, ... }) - predictTaskId.value = task_id -} -``` - -**关键点**： - -- `trainPoller.result.value` 才是 SUCCESS 后的完整 `TaskRecord`；`record.value` 是任意时刻（含中间态）的最新记录。模板里同时展示用 `trainPoller.record.value ?? trainPoller.result.value`。 -- `poller.isPolling.value` / `poller.status.value` / `poller.error.value` / `poller.taskId.value` 都是 `Ref`，模板里必须用 `.value`（它们是嵌套 ref，**Vue 模板不会自动 unwrap**）。 - ---- - -## 4. el-progress 状态映射 - -`PollerStatus = 'idle' | 'PENDING' | 'PROCESSING' | 'SUCCESS' | 'FAILED'` -`el-progress` 的 `status` 接受 `'' | 'success' | 'warning' | 'exception'`。 - -```ts -function progressOf(status: string): number { - switch (status) { - case 'idle': - case 'PENDING': return 10 - case 'PROCESSING':return 60 - case 'SUCCESS': - case 'FAILED': return 100 - default: return 0 - } -} -function progressStatusOf(s: string): '' | 'success' | 'exception' { - if (s === 'SUCCESS') return 'success' - if (s === 'FAILED') return 'exception' - return '' -} -``` - -模板里 `v-if="poller.isPolling.value || poller.status.value === 'SUCCESS' || poller.status.value === 'FAILED'"` 控制展示。 - ---- - -## 5. CSS：深色控制台风（slate 渐变 + 卡片玻璃态） - -```css -.app-root { - min-height: 100vh; - background: linear-gradient(180deg, #0f172a 0%, #1e293b 100%); - color: #e2e8f0; -} -.panel { - background: rgba(30, 41, 59, 0.7) !important; - border: 1px solid rgba(148, 163, 184, 0.18) !important; -} -.app-main { - display: grid; - grid-template-columns: 1fr 1fr; /* 左训练 / 右推断 */ - gap: 20px; -} -@media (max-width: 960px) { .app-main { grid-template-columns: 1fr; } } -``` - -深色背景下 Element Plus 的 `el-form-item__label` / `el-descriptions__label` 默认是黑色文字，必须 `:deep()` 覆盖成浅色。 - ---- - -## 6. 启动与验证 - -```bat -cd /d D:\111\office\ZHLduijie\1.WQ\WQ_GUI\frontend -npm install -npm run dev -``` - -打开 `http://127.0.0.1:5173/`，联调期望路径： - -1. 左侧「开始训练」→ 立即拿到 `task_id` + 黄色 `轮询中` + 进度条 60% -2. 后端 SUCCESS → 进度条变绿，下面出现 `model_id` 标签 + R²/RMSE/MAE -3. 右侧 `model_id` 被自动填入 → 「开始推断」→ 走 `output_zarr_path` 展示 -4. 任何一步 FAILED → 进度条变红 + 后端 `error` 字段 - ---- - -## 7. 已知 caveat - -- **第一次 `npm install` 约 150MB**，要耐心等。 -- `useTaskPoller` 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a/data/sub/waterindex.csv b/data/sub/waterindex.csv deleted file mode 100644 index 4a04128..0000000 --- a/data/sub/waterindex.csv +++ /dev/null @@ -1,46 +0,0 @@ -Formula_Name,Category,Formula,Reference -BGA_Am09KBBI,Phycocyanin (BGA_PC),(w686 - w658) / (w686 + w658),"Amin, R.; Zhou, J.; Gilerson, A.; Gross, B.; Moshary, F.; Ahmed, S.; Novel optical techniques for detecting and classifying toxic dinoflagellate Karenia brevis blooms using satellite imagery, Optics Express, 2009, 17, 11, 1-13." -BGA_Be162B643sub629,Phycocyanin (BGA_PC),w644 - w629,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538." -BGA_Be162B700sub601,Phycocyanin (BGA_PC),w700 - w601,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539." -BGA_Be162BsubPhy,Phycocyanin (BGA_PC),w715 - w615,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 540." -BGA_Be16FLHBlueRedNIR,Phycocyanin (BGA_PC),w658 - (w857 + (w458 - w857)),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538." -BGA_Be16FLHGreenRedNIR,Phycocyanin (BGA_PC),w658 - (w857 + (w558 - w857)),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539." -BGA_Be16FLHVioletRedNIR,Phycocyanin (BGA_PC),w658 - (w857 + (w444 - w857)),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538." -BGA_Be16MPI,Phycocyanin (BGA_PC),(w615 - w601) - (w644 - w601),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539." -BGA_Be16NDPhyI,Phycocyanin (BGA_PC),(w700 - w622) / (w700 + w622),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 540." -BGA_Be16NDPhyI644over615,Phycocyanin (BGA_PC),(w644 - w615) / (w644 + w615),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 541." -BGA_Be16NDPhyI644over629,Phycocyanin (BGA_PC),(w644 - w629) / (w644 + w629),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 542." -BGA_Be16Phy2BDA644over629,Phycocyanin (BGA_PC),w644 / w629,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 545." -BGA_Da052BDA,Phycocyanin (BGA_PC),w714 / w672,"Wynne, T. T., Stumpf, R. P., Tomlinson, M. C., Warner, R. A., Tester, P. A., Dyble, J.; Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes. Int. J. Remote Sens., 2008, 29, 3665-3672." -BGA_Go04MCI,Phycocyanin (BGA_PC),w709 - w681 - (w753 - w681),"Gower, J.F.R.; Brown,L.; Borstad, G.A.; Observation of chlorophyll fluorescence in west coast waters of Canada using the MODIS satellite sensor. Can. J. Remote Sens., 2004, 30 (1), 17?5." -BGA_HU103BDA,Phycocyanin (BGA_PC),(((1 / w615) - (1 / w600)) - w725),"Hunter, P.D.; Tyler, A.N.; Willby, N.J.; Gilvear, D.J.; The spatial dynamics of vertical migration by Microcystis aeruginosa in a eutrophic shallow lake: A case study using high spatial resolution time-series airborne remote sensing. Limn. Oceanogr. 2008, 53, 2391-2406" -BGA_Ku15PhyCI,Phycocyanin (BGA_PC),(-1 * (W681 - W665 - (W709 - W665))),"Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K., Guild, L.S.; Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters, Torres-Perez, J., 2015, Remote Sens. Environ., 2015, 167, 1-10." -BGA_Ku15SLH,Phycocyanin (BGA_PC),(w715 - w658) + (w715 - w658),"Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K., Guild, L.S.; Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters, Torres-Perez, J., 2015, Remote Sens. Environ., 2015, 167, 1-11." -BGA_MI092BDA,Phycocyanin (BGA_PC),w700 / w600,"Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for predicting PC concentrations in cyanobacteria: A proximal hyperspectral remote sensing approach. Remote Sens., 2009, 1, 758?75." -BGA_MM092BDA,Phycocyanin (BGA_PC),w724 / w600,"Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for predicting PC concentrations in cyanobacteria: A proximal hyperspectral remote sensing approach. Remote Sens., 2009, 1, 758?76." -BGA_MM12NDCIalt,Phycocyanin (BGA_PC),(w700 - w658) / (w700 + w658),"Mishra, S.; Mishra, D.R.; A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms, Env. Res. Lett., 2014, 9 (11), DOI:10.1088/1748-9326/9/11/114003" -BGA_MM143BDAopt,Phycocyanin (BGA_PC),((1 / w629) - (1 / w659)) * w724,"Mishra, S.; Mishra, D.R.; A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms, Env. Res. Lett., 2014, 9 (11), DOI:10.1088/1748-9326/9/11/114004" -BGA_SI052BDA,Phycocyanin (BGA_PC),w709 / w620,"Simis, S. G. H.; Peters, S.W. M.; Gons, H. J.; Remote sensing of the cyanobacteria pigment phycocyanin in turbid inland water. Limn. Oceanogr., 2005, 50, 237?45" -BGA_SM122BDA,Phycocyanin (BGA_PC),w709 / w600,"Mishra, S. 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Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538." -BGA_Be162B700sub601,Phycocyanin (BGA_PC),w700 - w601,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539." -BGA_Be162BsubPhy,Phycocyanin (BGA_PC),w715 - w615,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 540." -BGA_Be16FLHBlueRedNIR,Phycocyanin (BGA_PC),w658 - (w857 + (w458 - w857)),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538." -BGA_Be16FLHGreenRedNIR,Phycocyanin (BGA_PC),w658 - (w857 + (w558 - w857)),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539." -BGA_Be16FLHVioletRedNIR,Phycocyanin (BGA_PC),w658 - (w857 + (w444 - w857)),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538." -BGA_Be16MPI,Phycocyanin (BGA_PC),(w615 - w601) - (w644 - w601),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539." -BGA_Be16NDPhyI,Phycocyanin (BGA_PC),(w700 - w622) / (w700 + w622),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 540." -BGA_Be16NDPhyI644over615,Phycocyanin (BGA_PC),(w644 - w615) / (w644 + w615),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 541." -BGA_Be16NDPhyI644over629,Phycocyanin (BGA_PC),(w644 - w629) / (w644 + w629),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 542." -BGA_Be16Phy2BDA644over629,Phycocyanin (BGA_PC),w644 / w629,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 545." -BGA_Da052BDA,Phycocyanin (BGA_PC),w714 / w672,"Wynne, T. T., Stumpf, R. P., Tomlinson, M. C., Warner, R. A., Tester, P. A., Dyble, J.; Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes. Int. J. Remote Sens., 2008, 29, 3665-3672." -BGA_Go04MCI,Phycocyanin (BGA_PC),w709 - w681 - (w753 - w681),"Gower, J.F.R.; Brown,L.; Borstad, G.A.; Observation of chlorophyll fluorescence in west coast waters of Canada using the MODIS satellite sensor. Can. J. Remote Sens., 2004, 30 (1), 17?5." -BGA_HU103BDA,Phycocyanin (BGA_PC),(((1 / w615) - (1 / w600)) - w725),"Hunter, P.D.; Tyler, A.N.; Willby, N.J.; Gilvear, D.J.; The spatial dynamics of vertical migration by Microcystis aeruginosa in a eutrophic shallow lake: A case study using high spatial resolution time-series airborne remote sensing. Limn. Oceanogr. 2008, 53, 2391-2406" -BGA_Ku15PhyCI,Phycocyanin (BGA_PC),-1 * (W681 - W665 - (W709 - W665)),"Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K., Guild, L.S.; Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters, Torres-Perez, J., 2015, Remote Sens. Environ., 2015, 167, 1-10." -BGA_Ku15SLH,Phycocyanin (BGA_PC),(w715 - w658) + (w715 - w658),"Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K., Guild, L.S.; Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters, Torres-Perez, J., 2015, Remote Sens. Environ., 2015, 167, 1-11." -BGA_MI092BDA,Phycocyanin (BGA_PC),w700 / w600,"Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for predicting PC concentrations in cyanobacteria: A proximal hyperspectral remote sensing approach. Remote Sens., 2009, 1, 758?75." -BGA_MM092BDA,Phycocyanin (BGA_PC),w724 / w600,"Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for predicting PC concentrations in cyanobacteria: A proximal hyperspectral remote sensing approach. Remote Sens., 2009, 1, 758?76." -BGA_MM12NDCIalt,Phycocyanin (BGA_PC),(w700 - w658) / (w700 + w658),"Mishra, S.; Mishra, D.R.; A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms, Env. Res. Lett., 2014, 9 (11), DOI:10.1088/1748-9326/9/11/114003" -BGA_MM143BDAopt,Phycocyanin (BGA_PC),((1 / w629) - (1 / w659)) * w724,"Mishra, S.; Mishra, D.R.; A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms, Env. Res. Lett., 2014, 9 (11), DOI:10.1088/1748-9326/9/11/114004" -BGA_SI052BDA,Phycocyanin (BGA_PC),w709 / w620,"Simis, S. G. H.; Peters, S.W. M.; Gons, H. J.; Remote sensing of the cyanobacteria pigment phycocyanin in turbid inland water. Limn. Oceanogr., 2005, 50, 237?45" -BGA_SM122BDA,Phycocyanin (BGA_PC),w709 / w600,"Mishra, S. Remote sensing of cyanobacteria in turbid productive waters, PhD Dissertation. Mississippi State University, USA. 2012." -BGA_SY002BDA,Phycocyanin (BGA_PC),w650 / w625,"Schalles, J.; Yacobi, Y. Remote detection and seasonal patterns of phycocyanin, carotenoid and chlorophyll-a pigments in eutrophic waters. Archiv fur Hydrobiologie, Special Issues Advances in Limnology, 2000, 55,153?68" -BGA_Wy08CI,Phycocyanin (BGA_PC),-1 * (W686 - W672 - (W715 - W672)),"Wynne, T. T., Stumpf, R. P., Tomlinson, M. C., Warner, R. A., Tester, P. A., Dyble, J.; Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes. Int. J. Remote Sens., 2008, 29, 3665-3672." -Chl_Al10SABI,chlorophyll_a,(w857 - w644) / (w458 + w529),"Alawadi, F. Detection of surface algal blooms using the newly developed algorithm surface algal bloom index (SABI). Proc. SPIE 2010, 7825." -Chl_Am092Bsub,chlorophyll_a,w681 - w665,"Amin, R.; Zhou, J.; Gilerson, A.; Gross, B.; Moshary, F.; Ahmed, S. Novel optical techniques for detecting and classifying toxic dinoflagellate Karenia brevis blooms using satellite imagery. Opt. Express 2009, 17, 9126?144." -Chl_Be16FLHblue,chlorophyll_a,w529 - (w644 + (w458 - w644)),"Beck, R.A. and 22 others; Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations, Remote Sens. Environ., 2016, 178, 15-30." -Chl_Be16FLHviolet,chlorophyll_a,w529 - (w644 + (w429 - w644)),"Beck, R.A. and 22 others; Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations, Remote Sens. Environ., 2016, 178, 15-30." -Chl_Be16NDTIblue,chlorophyll_a,(w658 - w458) / (w658 + w458),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 543." -Chl_Be16NDTIviolet,chlorophyll_a,(w658 - w444) / (w658 + w444),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 544." -Chl_De933BDA,chlorophyll_a,w600 - w648 - w625,"Dekker, A.; Detection of the optical water quality parameters for eutrophic waters by high resolution remote sensing, Ph.D. thesis, 1993, Free University, Amsterdam." -Chl_Gi033BDA,chlorophyll_a,((1 / w672) - (1 / w715)) * w757,"Gitelson, A.A.; U. Gritz, and M. N. Merzlyak.; Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Phys. 2003, 160, 271-282." -Chl_Kn07KIVU,chlorophyll_a,(w458 - w644) / w529,"Kneubuhler, M.; Frank T.; Kellenberger, T.W; Pasche N.; Schmid M.; Mapping chlorophyll-a in Lake Kivu with remote sensing methods. 2007, Proceedings of the Envisat Symposium 2007, Montreux, Switzerland 23?7 April 2007 (ESA SP-636, July 2007)." -Chl_MM12NDCI,chlorophyll_a,(w715 - w686) / (w715 + w686),"Mishra, S.; and Mishra, D.R. Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters, Remote Sens. Environ., 2012, 117, 394-406" -Chl_Zh10FLH,chlorophyll_a,w686 - (w715 + (w672 - w751)),"Zhao, D.Z.; Xing, X.G.; Liu, Y.G.; Yang, J.H.; Wang, L. The relation of chlorophyll-a concentration with the reflectance peak near 700 nm in algae-dominated waters and sensitivity of fluorescence algorithms for detecting algal bloom. Int. J. Remote Sens. 2010, 31, 39-48" -Turb_Be16GreenPlusRedBothOverViolet,Turbidity,(w558 + w658) / w444,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538" -Turb_Be16RedOverViolet,Turbidity,w658 / w444,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539" -Turb_Bow06RedOverGreen,Turbidity,w658 / w558,"Bowers, D. G., and C. E. Binding. 2006. 鈥淭he Optical Properties of Mineral Suspended Particles: A Review and Synthesis.?Estuarine Coastal and Shelf Science 67 (1?): 219?30. doi:10.1016/j.ecss.2005.11.010" -Turb_Chip09NIROverGreen,Turbidity,w857 / w558,"Chipman, J. W.; Olmanson, L.G.; Gitelson, A.A.; Remote sensing methods for lake management: A guide for resource managers and decision-makers. 2009." -Turb_Dox02NIRoverRed,Turbidity,w857 / w658,"Doxaran, D., Froidefond, J.-M.; Castaing, P. ; A reflectance band ratio used to estimate suspended matter concentrations in sediment-dominated coastal waters, Remote Sens., 2002, 23, 5079-5085" -Turb_Frohn09GreenPlusRedBothOverBlue,Turbidity,(w558 + w658) / w458,"Frohn, R. C., & Autrey, B. C. (2009). Water quality assessment in the Ohio River using new indices for turbidity and chlorophyll-a with Landsat-7 Imagery. Draft Internal Report, US Environmental Protection Agency." -Turb_Harr92NIR,Turbidity,w857,"Schiebe F.R., Harrington J.A., Ritchie J.C. Remote-Sensing of Suspended Sediments鈥攖he Lake Chicot, Arkansas Project. Int. J. Remote Sens. 1992;13:1487?509" -Turb_Lath91RedOverBlue,Turbidity,w658 / w458,"Lathrop, R. G., Jr., T. M. Lillesand, and B. S. Yandell, 1991. Testing the utility of simple multi-date Thematic Mapper calibration algorithms for monitoring turbid inland waters. International Journal of Remote Sensing" -Turb_Moore80Red,Turbidity,w658,"Moore, G.K., Satellite remote sensing of water turbidity, Hydrological Sciences, 1980, 25, 4, 407-422" diff --git a/new/app/api/_smoke_test_train.py b/new/app/api/_smoke_test_train.py deleted file mode 100644 index 63404b0..0000000 --- a/new/app/api/_smoke_test_train.py +++ /dev/null @@ -1,201 +0,0 @@ -""" -冒烟测试 _run_train_sync: 用合成数据走通真实训练管线。 -不依赖 FastAPI / xarray / dask, 只验训练 + 持久化 + 回测。 -""" -import sys -import tempfile -from pathlib import Path - -import numpy as np -import pandas as pd - -# 绕过 main.py 触发 app 包导入（只导入 modeling 模块） -# 当前文件位于 new/app/api/_smoke_test_train.py -# app 包在 new/app/__init__.py, 故 new/ 必须在 sys.path 上 -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) - -from app.api.modeling import ( - _get_model_pipeline, - _load_train_df, - _resolve_feature_start, - _run_train_sync, - _MODEL_CLASS_REGISTRY, -) - - -def make_synthetic_csv(n_samples: int = 200, n_features: int = 8, noise: float = 0.1, seed: int = 42) -> Path: - """生成 [lat, lon, target, lat2, lon2, feat_0, feat_1, ...] 布局的 CSV""" - rng = np.random.default_rng(seed) - lat = rng.uniform(20, 25, n_samples) - lon = rng.uniform(110, 115, n_samples) - target = rng.uniform(0, 50, n_samples) - lat2 = rng.uniform(0, 1, n_samples) # 元数据 - lon2 = rng.uniform(0, 1, n_samples) # 元数据 - feats = rng.normal(0, 1, (n_samples, n_features)) - # 让 y 真正依赖前 3 个特征, RF 至少应该能学到 R² > 0.5 - feats[:, 0] += target / 10 - feats[:, 1] += target / 20 - feats[:, 2] -= target / 15 - - df = pd.DataFrame({ - "lat": lat, - "lon": lon, - "Chl-a": target, - "lat2": lat2, - "lon2": lon2, - **{f"feat_{i}": feats[:, i] for i in range(n_features)}, - }) - tmp = Path(tempfile.mkdtemp()) / "train.csv" - df.to_csv(tmp, index=False) - return tmp - - -def test_load_train_df(): - print("== test_load_train_df ==") - p = make_synthetic_csv(n_samples=50) - df = _load_train_df(str(p)) - assert df.shape == (50, 5 + 8), f"shape={df.shape}" - print(f" shape={df.shape}, columns[:6]={list(df.columns[:6])}") - print(" PASS") - - -def test_resolve_feature_start_int_and_str(): - print("== test_resolve_feature_start (int + str) ==") - p = make_synthetic_csv() - df = _load_train_df(str(p)) - idx_int = _resolve_feature_start(df, 5) - idx_str = _resolve_feature_start(df, "feat_0") - assert idx_int == 5 == idx_str, f"int={idx_int}, str={idx_str}" - print(f" int(5) -> {idx_int}, str('feat_0') -> {idx_str}") - print(" PASS") - - -def test_resolve_feature_start_str_miss(): - print("== test_resolve_feature_start (str 不存在 -> 抛错) ==") - p = make_synthetic_csv() - df = _load_train_df(str(p)) - try: - _resolve_feature_start(df, "not_exist") - print(" FAIL: 应抛 ValueError") - except ValueError as e: - print(f" 正确抛 ValueError: {e}") - print(" PASS") - - -def test_get_model_pipeline_all_types(): - print("== test_get_model_pipeline (5 种 model_type) ==") - for mt in ["RF", "SVR", "LinearRegression", "KNN", "PLS"]: - p = _get_model_pipeline(mt, {}) - assert len(p.steps) == 2 - assert p.steps[0][0] == "scaler" - assert p.steps[1][0] == "model" - print(f" 全部通过: {list(_MODEL_CLASS_REGISTRY)}") - print(" PASS") - - -def test_get_model_pipeline_bad_type(): - print("== test_get_model_pipeline (坏 model_type) ==") - try: - _get_model_pipeline("XGBoost", {}) - print(" FAIL: 应抛 ValueError") - except ValueError as e: - print(f" 正确抛 ValueError: {e}") - print(" PASS") - - -def test_run_train_sync_rf_end_to_end(): - print("== test_run_train_sync (RF 端到端) ==") - p = make_synthetic_csv(n_samples=200) - out_dir = Path(tempfile.mkdtemp()) - out_path = out_dir / "model.joblib" - - import time - t0 = time.time() - metadata = _run_train_sync( - model_type="RF", - target="Chl-a", - train_data_path=str(p), - feature_start=5, - params={"n_estimators": 30, "max_depth": 6, "random_state": 42, "n_jobs": 1}, - output_model_path=out_path, - ) - dt = time.time() - t0 - - assert out_path.exists(), f"joblib 未落盘: {out_path}" - print(f" joblib 落盘: {out_path} ({out_path.stat().st_size} bytes)") - print(f" metadata.test_r2={metadata['test_r2']:.4f} test_rmse={metadata['test_rmse']:.4f} test_mae={metadata['test_mae']:.4f}") - print(f" metadata.n_features={metadata['n_features']} n_samples={metadata['n_samples']} train_size={metadata['train_size']} test_size={metadata['test_size']}") - print(f" 耗时 {dt:.2f}s") - - # 回测: 加载 joblib 再 predict - import joblib - saved = joblib.load(out_path) - assert "model" in saved and "metadata" in saved, f"joblib 双 key 缺失: {saved.keys()}" - assert hasattr(saved["model"], "predict") - assert saved["metadata"]["test_r2"] == metadata["test_r2"] - print(f" joblib 加载 OK, 含 'model' 和 'metadata' 双 key") - print(" PASS") - - -def test_run_train_sync_linearregression_fast(): - print("== test_run_train_sync (LinearRegression 快速路径) ==") - p = make_synthetic_csv(n_samples=150) - out_path = Path(tempfile.mkdtemp()) / "lr.joblib" - metadata = _run_train_sync( - model_type="LinearRegression", - target="Chl-a", - train_data_path=str(p), - feature_start=5, - params={}, - output_model_path=out_path, - ) - print(f" test_r2={metadata['test_r2']:.4f} (LR 学到线性, R² 应 >= 0.4)") - assert metadata["test_r2"] > 0.3, f"LR test_r2={metadata['test_r2']} 太低, 数据生成可能有问题" - print(" PASS") - - -def test_run_train_sync_bad_csv(): - print("== test_run_train_sync (CSV 不存在) ==") - try: - _run_train_sync("RF", "Chl-a", "/no/such/path.csv", 5, {}, Path("/tmp/x.joblib")) - print(" FAIL: 应抛异常") - except (FileNotFoundError, ValueError) as e: - print(f" 正确抛 {type(e).__name__}: {e}") - print(" PASS") - - -def test_run_train_sync_bad_target(): - print("== test_run_train_sync (target 列不存在) ==") - p = make_synthetic_csv() - try: - _run_train_sync("RF", "NopeTarget", str(p), 5, {}, Path("/tmp/x.joblib")) - print(" FAIL: 应抛 ValueError") - except ValueError as e: - print(f" 正确抛 ValueError: {e}") - print(" PASS") - - -def test_run_train_sync_str_feature_start(): - print("== test_run_train_sync (feature_start 用列名) ==") - p = make_synthetic_csv() - out_path = Path(tempfile.mkdtemp()) / "str_fs.joblib" - metadata = _run_train_sync("RF", "Chl-a", str(p), "feat_0", {"n_estimators": 10}, out_path) - assert metadata["feature_start"] == "feat_0" - assert metadata["n_features"] == 8 - assert metadata["feature_columns"][0] == "feat_0" - print(f" 列名 'feat_0' 解析正确, n_features={metadata['n_features']}") - print(" PASS") - - -if __name__ == "__main__": - test_load_train_df() - test_resolve_feature_start_int_and_str() - test_resolve_feature_start_str_miss() - test_get_model_pipeline_all_types() - test_get_model_pipeline_bad_type() - test_run_train_sync_rf_end_to_end() - test_run_train_sync_linearregression_fast() - test_run_train_sync_bad_csv() - test_run_train_sync_bad_target() - test_run_train_sync_str_feature_start() - print("\n>>> ALL SMOKE TESTS PASSED") diff --git a/new/app/api/endpoints.py b/new/app/api/endpoints.py deleted file mode 100644 index 4a012f1..0000000 --- a/new/app/api/endpoints.py +++ /dev/null @@ -1,222 +0,0 @@ -""" -API 路由集合 -============ - -把业务接口统一收口到 APIRouter，再由 main.py 通过 include_router 挂载。 - -当前包含的接口： - GET /api/algorithms 列出已注册的所有去耀斑算法（供前端下拉框） - POST /api/process/deglint 提交去耀斑处理任务，立即返回 task_id - GET /api/tasks/{task_id} 查询指定任务的状态与结果 - -派发链： - POST /api/process/deglint - └─ BackgroundTasks.add_task(execute_glint_removal_task, ...) - └─ get_remover(method) 从注册表拿到算法类 - └─ remover.process(input_zarr, output_zarr, **params) -""" - -import traceback -import uuid -from datetime import datetime -from typing import Any, Dict - -from fastapi import APIRouter, BackgroundTasks, HTTPException -from pydantic import BaseModel, Field - -# 并发安全的任务状态存储（替代旧版的 MOCK_TASK_DB） -from app.core.task_store import get_task, set_task, update_task - -# 算法注册表 API -from app.core.algorithms import get_remover, list_removers - - -# --------------------------------------------------------------------------- -# 路由实例 -# --------------------------------------------------------------------------- -# prefix 不在此处设置，统一在 main.py 挂载时给定，便于将来按版本拆分 -# （例如 /api/v1、/api/v2 共存时复用同一个 router 对象）。 -# --------------------------------------------------------------------------- -router = APIRouter(tags=["deglint"]) - - -# --------------------------------------------------------------------------- -# 请求 / 响应数据模型 -# --------------------------------------------------------------------------- -class DeglintRequest(BaseModel): - """POST /api/process/deglint 的请求体""" - - method: str = Field( - ..., - description="去耀斑方法名称，必须是已注册算法，例如 'kutser' / 'goodman'", - examples=["kutser"], - ) - params: Dict[str, Any] = Field( - default_factory=dict, - description=( - "传递给算法 process() 的超参数字典，例如 " - "Kutser: {'band_lower': 773, 'band_oxy': 845, 'band_upper': 893}; " - "Goodman: {'band_ref': 750, 'band_diff': 640, 'A': 0.0, 'B': 0.0}" - ), - examples=[{"band_lower": 773, "band_oxy": 845, "band_upper": 893}], - ) - - -class TaskAcceptedResponse(BaseModel): - """提交任务成功后立即返回的响应""" - - task_id: str - status: str # 一定是 PENDING - - -class AlgorithmListResponse(BaseModel): - """GET /api/algorithms 的响应""" - - algorithms: list # 已注册算法名列表 - count: int # 算法总数 - - -# --------------------------------------------------------------------------- -# 后台任务执行器（真实派发链） -# --------------------------------------------------------------------------- -# 注意：这里使用 async def。 -# FastAPI / Starlette 的 BackgroundTasks 支持 async function， -# 会在响应返回后自动 await 它，不影响主请求链路。 -# --------------------------------------------------------------------------- -async def execute_glint_removal_task( - task_id: str, - method: str, - params: Dict[str, Any], -) -> None: - """ - 后台异步执行器：按 method 名字从注册表取出算法类，实例化并运行 process()。 - - 状态机： - PENDING -> PROCESSING -> SUCCESS - └──> FAILED（含 error / traceback） - """ - # 0. 安全检查：任务记录必须已存在（POST 阶段已写入） - record = await get_task(task_id) - if record is None: - print(f"[{task_id}] 任务不存在, 跳过") - return - - # 1. 状态推进到 PROCESSING - await update_task( - task_id, - status="PROCESSING", - updated_at=datetime.now().isoformat(), - ) - print(f"[{task_id}] 开始处理 method={method} params={params}") - - # 2. 临时硬编码 IO 路径（未来由数据管理层提供） - # TODO: 替换为真实的数据管理服务返回的 zarr 路径 - input_zarr_path = "./data/temp_in.zarr" - output_zarr_path = f"./data/{task_id}_out.zarr" - - try: - # 3. 按 method 名字从注册表取算法类并实例化 - # get_remover 找不到时会抛 KeyError，下面的 except 会兜住 - algorithm_cls = get_remover(method) - remover = algorithm_cls() - - # 4. 调用算法（注意 await，因为 BaseGlintRemover.process 是 async） - await remover.process(input_zarr_path, output_zarr_path, **params) - - # 5. 成功：写回结果路径与状态 - await update_task( - task_id, - status="SUCCESS", - output_zarr_path=output_zarr_path, - error=None, - updated_at=datetime.now().isoformat(), - ) - print(f"[{task_id}] 处理完成 -> SUCCESS, output={output_zarr_path}") - - except Exception as exc: # noqa: BLE001 顶层兜底，绝不让后台任务静默失败 - # 6. 失败：记录错误信息与堆栈，便于前端排查 - await update_task( - task_id, - status="FAILED", - output_zarr_path=None, - error=f"{type(exc).__name__}: {exc}", - traceback=traceback.format_exc(), - updated_at=datetime.now().isoformat(), - ) - print(f"[{task_id}] 处理失败 -> {type(exc).__name__}: {exc}") - - -# --------------------------------------------------------------------------- -# GET /algorithms -# --------------------------------------------------------------------------- -# 返回当前已注册的所有算法名，供前端动态渲染下拉框 / 选择器。 -# --------------------------------------------------------------------------- -@router.get("/algorithms", response_model=AlgorithmListResponse) -async def list_registered_algorithms() -> Dict[str, Any]: - """列出已注册的去耀斑算法。""" - names = list(list_removers().keys()) - return {"algorithms": names, "count": len(names)} - - -# --------------------------------------------------------------------------- -# POST /process/deglint -# --------------------------------------------------------------------------- -# 提交去耀斑处理任务。FastAPI 在函数返回后才会把响应发给前端， -# 因此通过 BackgroundTasks 把耗时操作丢到后台，接口本身立刻返回 task_id。 -# --------------------------------------------------------------------------- -@router.post("/process/deglint", response_model=TaskAcceptedResponse) -async def submit_deglint( - payload: DeglintRequest, - background_tasks: BackgroundTasks, -) -> Dict[str, Any]: - """提交一个去耀斑处理任务，并立即返回 task_id。""" - - # 1. 生成唯一任务 ID（UUID4 足以保证全局唯一性） - task_id = str(uuid.uuid4()) - - # 2. 在任务库中登记一条 PENDING 记录（并发安全） - # 注意：output_zarr_path / error / traceback 字段在执行过程中被填充 - await set_task( - task_id, - { - "task_id": task_id, - "method": payload.method, - "params": payload.params, - "status": "PENDING", - "output_zarr_path": None, - "error": None, - "traceback": None, - "created_at": datetime.now().isoformat(), - "updated_at": datetime.now().isoformat(), - }, - ) - - # 3. 把真实执行器丢到后台 - background_tasks.add_task( - execute_glint_removal_task, - task_id, - payload.method, - payload.params, - ) - - # 4. 立即返回 task_id 与 PENDING 状态 - return {"task_id": task_id, "status": "PENDING"} - - -# --------------------------------------------------------------------------- -# GET /tasks/{task_id} -# --------------------------------------------------------------------------- -# 前端轮询此接口获取任务状态。PENDING / PROCESSING 表示仍在跑， -# SUCCESS 表示成功（含 output_zarr_path），FAILED 表示失败（含 error / traceback）。 -# --------------------------------------------------------------------------- -@router.get("/tasks/{task_id}") -async def get_task_status(task_id: str) -> Dict[str, Any]: - """查询指定任务的当前状态与结果。""" - - record = await get_task(task_id) - if record is None: - # 找不到 task_id 通常意味着客户端拼错了 ID，或者记录已被清理 - raise HTTPException(status_code=404, detail=f"task_id 不存在: {task_id}") - - # 直接返回字典，FastAPI 会自动 JSON 序列化 - return record diff --git a/new/app/api/modeling.py b/new/app/api/modeling.py deleted file mode 100644 index 01ac34f..0000000 --- a/new/app/api/modeling.py +++ /dev/null @@ -1,847 +0,0 @@ -""" -app/api/modeling.py -=================== - -机器学习与水质反演相关的 API 路由。 - -接口（最终路径, 挂载后）： - POST /api/modeling/train 提交模型训练任务, 立即返回 task_id - GET /api/modeling/models 列出已训练好的模型（未来从磁盘 joblib 读） - POST /api/modeling/predict 提交模型推断任务, 立即返回 task_id - -设计要点 --------- -- 训练 / 推断均为异步后台任务, 复用 app.core.task_store 的并发安全任务状态。 -- 模型元数据用模块级 _MODEL_REGISTRY 暂存（开发期内存存储）, - 未来从磁盘 joblib 读时只需替换 list_trained_models() 内部实现即可。 -- /predict 已接入真实 sklearn + xarray + dask 流式推断: - * joblib.load 读模型（缺文件时降级为 Dummy RandomForestRegressor） - * xr.open_zarr 延迟打开影像, NaN 填 0 - * xr.apply_ufunc(dask="parallelized") 沿 (y, x) 逐 chunk 调 model.predict - * to_zarr(mode="w", compute=True) 流式写出, 内存峰值 ≈ 1 个 chunk -- /train 已接入真实 sklearn + pandas 训练管线: - * pd.read_csv 读结构化训练表（支持 [lat, lon, target_*, feature_*] 布局） - * 按 target 列 dropna 清洗；按 feature_start 索引/列名切分特征 - * sklearn Pipeline: StandardScaler -> {RF/SVR/LinearRegression/KNN/PLS} - * train_test_split(80/20) 划分, 计算 test_r2/rmse/mae - * joblib.dump({model, metadata}) 落盘 ./data/models/{model_id}.joblib - * 测试指标写回 TASK_STORE, 同时登记到 _MODEL_REGISTRY - 注: 旧版 SPXY / KS 划分留作未来扩展, 当前固定 random 划分 (test_size=0.2, random_state=42)。 -""" - -import asyncio -import shutil -import traceback -import uuid -from datetime import datetime -from pathlib import Path -from typing import Any, Dict, List, Optional, Union - -import joblib -import numpy as np -import pandas as pd -import xarray as xr -from fastapi import APIRouter, BackgroundTasks, HTTPException, UploadFile, File -from pydantic import BaseModel, Field -from sklearn.cross_decomposition import PLSRegression -from sklearn.ensemble import RandomForestRegressor -from sklearn.linear_model import LinearRegression -from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score -from sklearn.model_selection import train_test_split -from sklearn.neighbors import KNeighborsRegressor -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import StandardScaler -from sklearn.svm import SVR - -# 复用并发安全任务状态存储（与 deglint 共享同一份 TASK_STORE, -# 通过 task 记录里的 "kind" 字段区分 train / predict / deglint） -from app.core.task_store import get_task, set_task, update_task - - -# --------------------------------------------------------------------------- -# 路由实例 -# --------------------------------------------------------------------------- -# prefix="/modeling" 让本文件内只写 /train /models /predict 等短路径, -# 最终完整路径由 main.py 挂载时再补 /api。 -# --------------------------------------------------------------------------- -router = APIRouter(prefix="/modeling", tags=["modeling"]) - - -# --------------------------------------------------------------------------- -# 数据模型 -# --------------------------------------------------------------------------- -class TrainRequest(BaseModel): - """POST /api/modeling/train 的请求体""" - - model_type: str = Field( - ..., - description="模型类型, 例如 'RF' (随机森林) / 'SVR' (支持向量回归) / 'XGBoost' / 'MLP'", - examples=["RF", "SVR"], - ) - target: str = Field( - ..., - description="反演目标水质参数, 例如 'Chl-a' (叶绿素a) / 'TSS' (总悬浮物) / 'CDOM' (有色可溶有机物)", - examples=["Chl-a", "TSS", "CDOM"], - ) - train_data_path: str = Field( - ..., - description="训练数据集的 zarr 路径（包含 reflectance 变量与 target 标签）", - examples=["./data/train.zarr"], - ) - feature_start: Union[int, str] = Field( - default=4, - description=( - "特征列起始位置. 表格布局假定为 " - "[lat, lon, target_1, target_2, ..., feature_1, feature_2, ...] " - "可传 int 列索引（如 4）或 str 列名（如 '374.285' 波长起点）。" - "默认 4, 即前 4 列视为元数据/目标, 之后全部是特征。" - ), - examples=[4, "374.285"], - ) - params: Dict[str, Any] = Field( - default_factory=dict, - description="模型超参, 例如 RF 的 {'n_estimators': 100, 'max_depth': 20}", - examples=[{"n_estimators": 100, "max_depth": 20}], - ) - - -class PredictRequest(BaseModel): - """POST /api/modeling/predict 的请求体""" - - model_id: str = Field( - ..., - description="已训练模型的 ID（由 /api/modeling/train 返回或 /api/modeling/models 列出）", - ) - input_zarr_path: str = Field( - ..., - description="待推断影像的 zarr 路径", - examples=["./data/scene.zarr"], - ) - output_zarr_path: Optional[str] = Field( - default=None, - description=( - "输出 zarr 路径, 缺省时由后端按规则生成 " - "(如 ./data/{model_id}_{input_stem}_pred.zarr)" - ), - ) - - -class TaskAcceptedResponse(BaseModel): - """提交训练/推断任务后立即返回的响应""" - - task_id: str - status: str # 一定是 PENDING - kind: str # "train" / "predict", 便于前端识别任务类型 - - -class ModelInfo(BaseModel): - """单个模型的元信息（GET /api/modeling/models 的元素）""" - - model_id: str - model_type: str - target: str - params: Dict[str, Any] - path: str # joblib 文件路径 - created_at: str - train_task_id: str # 产生此模型的那个训练任务的 ID - - -class ModelListResponse(BaseModel): - """GET /api/modeling/models 的响应""" - - models: List[ModelInfo] - count: int - - -# --------------------------------------------------------------------------- -# 模块级模型注册表（开发期内存, 未来替换为磁盘扫描） -# --------------------------------------------------------------------------- -# model_id -> ModelInfo 字典 -# 读多写少, 用一个普通 dict 足够（CPython GIL 兜底）。 -# 写时（训练完成时）只发生一次, 无并发风险。 -# --------------------------------------------------------------------------- -_MODEL_REGISTRY: Dict[str, Dict[str, Any]] = {} - - -def _register_model(record: Dict[str, Any]) -> None: - """将训练完成的模型登记到内存注册表。""" - _MODEL_REGISTRY[record["model_id"]] = record - - -# --------------------------------------------------------------------------- -# 训练管线的模块级辅助函数 -# --------------------------------------------------------------------------- -# 设计要点 (与推断管线一致): -# 1) 模块级函数: dask / joblib 后端若走子进程 pickle, 嵌套闭包会丢字段。 -# 2) 同步执行: execute_train_task 用 asyncio.to_thread 派发, 内部全程同步阻塞。 -# 3) 失败抛异常: 异常由 execute_train_task 捕获, 转 FAILED + traceback。 -# --------------------------------------------------------------------------- - -# model_type (大写字符串) -> sklearn 估计器类 -# 与 OpenClaw model_configs 思路一致, 但此处只保留类 (参数由 params 透传) -_MODEL_CLASS_REGISTRY: Dict[str, type] = { - "RF": RandomForestRegressor, - "SVR": SVR, - "LinearRegression": LinearRegression, - "KNN": KNeighborsRegressor, - "PLS": PLSRegression, -} - - -def _get_model_pipeline(model_type: str, params: Optional[Dict[str, Any]]) -> Pipeline: - """ - 模型工厂: 按 model_type 选 sklearn 类, 用 StandardScaler + 估计器构造 Pipeline。 - - 与 OpenClaw 不同之处: 把 scaler 放进 Pipeline 第一步, - 推断时直接 pipeline.predict(X) 即可, scaler 参数与训练时严格一致。 - """ - model_cls = _MODEL_CLASS_REGISTRY.get(model_type) - if model_cls is None: - raise ValueError( - f"不支持的 model_type='{model_type}', " - f"可选: {sorted(_MODEL_CLASS_REGISTRY.keys())}" - ) - estimator = model_cls(**(params or {})) - return Pipeline([("scaler", StandardScaler()), ("model", estimator)]) - - -def _load_train_df(csv_path: str) -> pd.DataFrame: - """ - 读 CSV 训练表, 规整空串 / 空白 / NULL 等为 NaN。 - - 沿用 OpenClaw modeling_batch.load_data_batch 的读取策略: - na_values 显式列举 + 正则二次清理 (防 cell 内出现 " " 等纯空白)。 - """ - try: - df = pd.read_csv( - csv_path, - na_values=["", " ", "NaN", "nan", "NULL", "null"], - ) - except FileNotFoundError as exc: - raise FileNotFoundError(f"训练数据文件不存在: {csv_path}") from exc - except pd.errors.EmptyDataError as exc: - raise ValueError(f"训练数据文件为空: {csv_path}") from exc - # 二次清理: 残留的纯空白 cell - df = df.replace(r"^\s*$", np.nan, regex=True) - return df - - -def _resolve_feature_start( - df: pd.DataFrame, - feature_start: Union[int, str], -) -> int: - """ - 将 feature_start (int 索引 / str 列名) 统一解析为 int 列索引。 - - 与 OpenClaw modeling_batch.load_data_batch / load_data_single 一致: - str 走 columns.get_loc, int 直接返回。 - """ - if isinstance(feature_start, str): - if feature_start not in df.columns: - raise ValueError( - f"feature_start='{feature_start}' 不在 CSV 列中: {list(df.columns)}" - ) - return int(df.columns.get_loc(feature_start)) - return int(feature_start) - - -def _run_train_sync( - model_type: str, - target: str, - train_data_path: str, - feature_start: Union[int, str], - params: Optional[Dict[str, Any]], - output_model_path: Path, -) -> Dict[str, Any]: - """ - 完整同步训练流程 (由 execute_train_task 在线程池内调用): - - pd.read_csv -> 目标列 dropna -> 切特征 -> train_test_split(80/20) - -> Pipeline(StandardScaler + model).fit -> 评估 test_r2/rmse/mae - -> joblib.dump({model, metadata}, output_model_path) - - Returns: - metadata 字典, 含 test_r2 / test_rmse / test_mae / n_features 等, - 调用方负责写回 TASK_STORE 和 _MODEL_REGISTRY。 - - 注: 旧版 SPXY / KS 划分留作未来扩展 (params.split_method 控制), - 当前固定 random + test_size=0.2 + random_state=42。 - """ - df = _load_train_df(train_data_path) - - if target not in df.columns: - raise ValueError( - f"target='{target}' 不在 CSV 列中, 可选: {list(df.columns)}" - ) - - # 1) 清洗: 仅剔除 target NaN 的行 (与 OpenClaw load_data_single 一致) - df = df[df[target].notna()].copy() - if df.empty: - raise ValueError("target 剔除 NaN 后无样本, 终止训练") - - # 2) 特征切分 - feature_start_idx = _resolve_feature_start(df, feature_start) - feature_columns = list(df.columns[feature_start_idx:]) - - X = df.iloc[:, feature_start_idx:].astype(np.float64) - y = df[target].astype(np.float64).values - - # 3) 划分 (固定 random, 未来扩展 spxy/ks) - X_train, X_test, y_train, y_test = train_test_split( - X.values, - y, - test_size=0.2, - random_state=42, - ) - - # 4) 构造 Pipeline + 训练 - pipeline = _get_model_pipeline(model_type, params) - pipeline.fit(X_train, y_train) - - # 5) 测试集与训练集评估 - y_pred = pipeline.predict(X_test) - test_r2 = float(r2_score(y_test, y_pred)) - test_rmse = float(np.sqrt(mean_squared_error(y_test, y_pred))) - test_mae = float(mean_absolute_error(y_test, y_pred)) - - y_train_pred = pipeline.predict(X_train) - train_r2 = float(r2_score(y_train, y_train_pred)) - train_rmse = float(np.sqrt(mean_squared_error(y_train, y_train_pred))) - train_mae = float(mean_absolute_error(y_train, y_train_pred)) - - metadata: Dict[str, Any] = { - "model_type": model_type, - "target": target, - "feature_start": feature_start, - "feature_columns": feature_columns, - "n_features": int(X.shape[1]), - "n_samples": int(X.shape[0]), - "train_size": int(X_train.shape[0]), - "test_size": int(X_test.shape[0]), - "params": dict(params or {}), - "test_r2": test_r2, - "test_rmse": test_rmse, - "test_mae": test_mae, - "train_r2": train_r2, - "train_rmse": train_rmse, - "train_mae": train_mae, - "split_method": "random", - "trained_at": datetime.now().isoformat(), - } - - # 7) 持久化 (目录可能不存在, 顺手建) - output_model_path = Path(output_model_path) - output_model_path.parent.mkdir(parents=True, exist_ok=True) - joblib.dump( - {"model": pipeline, "metadata": metadata}, - output_model_path, - ) - - return metadata - - -# --------------------------------------------------------------------------- -# 推断管线的模块级辅助函数 -# --------------------------------------------------------------------------- -# 设计要点: -# 1) Dask 调度时, 函数必须可被工作进程 pickle 序列化。 -# 因此 _predict_block / _load_model / _make_dummy_model / _run_predict_sync -# 全部是模块级函数 (而非嵌套), 避免闭包陷阱。 -# 2) _predict_block 通过 model.predict(spectra_2d) 整批预测, -# 整张影像的 O(n_pixels * n_bands) 一次性预测在大矩阵上必 OOM, -# 因此外层用 xr.apply_ufunc(dask="parallelized") 把矩阵切块 -# 逐块进入此函数, 单次内存峰值 ≈ 1 个 (y_chunk, x_chunk, band) 大小。 -# --------------------------------------------------------------------------- - - -def _make_dummy_model(n_features: int) -> RandomForestRegressor: - """ - 构造一个 Dummy 随机森林回归器。 - - 用途: - 1) 真实 joblib 文件不存在时的连通性测试 - 2) 训练骨架尚未接入真实数据时的占位推断 - """ - rng = np.random.default_rng(42) - X = rng.random((200, n_features)) - y = rng.random(200) - model = RandomForestRegressor( - n_estimators=10, max_depth=5, random_state=0, n_jobs=1 - ) - model.fit(X, y) - return model - - -def _load_model(path: str, n_features: int) -> Any: - """ - 加载训练好的 sklearn 模型, 失败时降级 Dummy。 - - 优先级: - 1) path 存在且 joblib.load 成功 -> 返回真实模型 - 2) 否则 -> 降级为 Dummy 随机森林 (n_features 必须指定) - """ - p = Path(path) - if p.is_file() and p.stat().st_size > 0: - try: - print(f"[model] 从磁盘加载: {path}") - return joblib.load(path) - except Exception as exc: # noqa: BLE001 - print(f"[model] joblib.load 失败 ({type(exc).__name__}: {exc}), 降级 Dummy") - print(f"[model] 真实 joblib 不存在 ({path}), 使用 Dummy RandomForest") - return _make_dummy_model(n_features) - - -def _predict_block(spectra_3d: np.ndarray, model: Any) -> np.ndarray: - """ - 单个 dask chunk 的推断函数 (xr.apply_ufunc 会自动调度调用)。 - - Parameters - ---------- - spectra_3d : np.ndarray - 形状 (y_chunk, x_chunk, n_bands)。 - 此形状由 input_core_dims=[["band"]] 决定: - xarray 会把 band 维移到最后一轴, 然后按 (y, x) 的 chunk 切分调用本函数。 - model : 已 fit 好的 sklearn 估计器 - 接受 (n_samples, n_features) 输入, 返回 (n_samples,) 预测。 - - Returns - ------- - np.ndarray - 形状 (y_chunk, x_chunk), dtype float32 的标量预测图。 - """ - yc, xc, nb = spectra_3d.shape - # 2D 化: 每个像素一行光谱 - flat = spectra_3d.reshape(yc * xc, nb) - # sklearn 风格的批量预测 - pred = model.predict(flat) - # 还原为 2D 空间图, 强制 float32 节约一半内存 - return pred.reshape(yc, xc).astype(np.float32, copy=False) - - -def _run_predict_sync( - model: Any, - model_id: str, - input_zarr_path: str, - output_zarr_path: str, -) -> None: - """ - 同步推断主流程 (被 asyncio.to_thread 调用)。 - - 流程: - 1) xr.open_zarr 延迟打开 (dask 数组, 不一次性读入内存) - 2) NaN -> 0 清洗 (model.predict 不接受 NaN) - 3) xr.apply_ufunc 沿 (y, x) 逐 chunk 调 _predict_block - 4) 非水域置 NaN (zarr 支持 float NaN) - 5) to_zarr 触发整图计算 + 流式写出 - """ - # 1. 延迟打开输入 (关键: Dask 不一次性读入内存) - ds = xr.open_zarr(input_zarr_path, chunks="auto") - if "reflectance" not in ds.data_vars: - raise KeyError( - f"输入 zarr 缺少 'reflectance' 变量; 实际: {list(ds.data_vars)}" - ) - - reflectance = ds["reflectance"] # dims: (y, x, band) - n_bands = reflectance.sizes["band"] - - # 2. 水域掩膜 (与去耀斑算法同约定) - if "water_mask" in ds.data_vars or "water_mask" in ds.coords: - water_mask = ds["water_mask"].astype(bool) - else: - water_mask = xr.ones_like(reflectance.isel(band=0), dtype=bool) - - # 3. NaN 清洗: 填充 0 (model.predict 不接受 NaN) - refl_clean = reflectance.fillna(0.0) - - # 4. 核心: 用 apply_ufunc 把 model.predict 沿 (y, x) 应用 - # dask="parallelized" 让每个 (y_chunk, x_chunk, band) chunk - # 独立调 _predict_block, 任意时刻内存中只有若干个 chunk。 - prediction: xr.DataArray = xr.apply_ufunc( - _predict_block, - refl_clean, - kwargs={"model": model}, - input_core_dims=[["band"]], - output_core_dims=[[]], - dask="parallelized", - output_dtypes=[np.float32], - dask_gufunc_kwargs={"allow_rechunk": True}, - vectorize=False, - ) - - # 5. 非水域置 NaN (zarr 支持 float NaN, 便于后续可视化/掩膜分析) - prediction = prediction.where(water_mask, np.nan) - - # 6. 包装为 Dataset 并流式写出 - out = xr.Dataset( - {"prediction": prediction}, - attrs={ - "model_id": model_id, - "input_zarr_path": input_zarr_path, - "n_bands": n_bands, - "created_at": datetime.now().isoformat(), - }, - ) - # 保留 y/x 坐标 - out = out.assign_coords(y=ds["y"], x=ds["x"]) - - # to_zarr + compute=True 触发整图 dask 图求值 - # 中间会按 chunk 逐块调度到线程池, 内存峰值 ≈ 1 个 chunk 的体量 - out.to_zarr(output_zarr_path, mode="w", compute=True) - - -# --------------------------------------------------------------------------- -# 后台任务执行器 -# --------------------------------------------------------------------------- -async def execute_train_task( - task_id: str, - model_type: str, - target: str, - train_data_path: str, - feature_start: Union[int, str], - params: Dict[str, Any], -) -> None: - """ - 训练任务后台执行器（已接入真实 sklearn 训练流程）。 - - 流程: - 1) get_task 校验任务存在 - 2) update_task(PROCESSING) - 3) 生成 model_id / model_path - 4) asyncio.to_thread 派发 _run_train_sync 到默认线程池 - 5) 成功 -> _register_model + update_task(SUCCESS, 附 test_r2/rmse/mae) - 6) 失败 -> update_task(FAILED, 附 error + traceback) - """ - record = await get_task(task_id) - if record is None: - print(f"[{task_id}] 训练任务不存在, 跳过") - return - - await update_task( - task_id, - status="PROCESSING", - updated_at=datetime.now().isoformat(), - ) - print( - f"[{task_id}] 开始训练 model_type={model_type} target={target} " - f"train_data_path={train_data_path} feature_start={feature_start}" - ) - - # model_id 用 uuid4 前 12 位 (8 位易撞, 12 位兼顾可读性) - model_id = f"model_{uuid.uuid4().hex[:12]}" - model_path = Path(f"./data/models/{model_id}.joblib") - - try: - # 同步 sklearn / pandas 训练丢到默认线程池, 不阻塞 event loop - metadata = await asyncio.to_thread( - _run_train_sync, - model_type, - target, - train_data_path, - feature_start, - params, - model_path, - ) - - # 登记到内存注册表 (供 /predict 查 model_id) - _register_model( - { - "model_id": model_id, - "model_type": model_type, - "target": target, - "params": dict(params or {}), - "path": str(model_path), - "feature_start": feature_start, - "n_features": metadata["n_features"], - "test_r2": metadata["test_r2"], - "test_rmse": metadata["test_rmse"], - "test_mae": metadata["test_mae"], - "created_at": datetime.now().isoformat(), - "train_task_id": task_id, - } - ) - - # 把训练指标写回任务记录, 前端轮询时可直接看 - await update_task( - task_id, - status="SUCCESS", - model_id=model_id, - model_path=str(model_path), - test_r2=metadata["test_r2"], - test_rmse=metadata["test_rmse"], - test_mae=metadata["test_mae"], - n_features=metadata["n_features"], - n_samples=metadata["n_samples"], - error=None, - traceback=None, - updated_at=datetime.now().isoformat(), - ) - print( - f"[{task_id}] 训练完成 -> model_id={model_id} " - f"test_r2={metadata['test_r2']:.4f} test_rmse={metadata['test_rmse']:.4f}" - ) - - except Exception as exc: # noqa: BLE001 - # 失败时 model_path 不一定有产物, 显式置 None 方便前端判断 - await update_task( - task_id, - status="FAILED", - model_id=None, - model_path=None, - error=f"{type(exc).__name__}: {exc}", - traceback=traceback.format_exc(), - updated_at=datetime.now().isoformat(), - ) - print(f"[{task_id}] 训练失败 -> {type(exc).__name__}: {exc}") - - -async def execute_predict_task( - task_id: str, - model_id: str, - input_zarr_path: str, - output_zarr_path: Optional[str], -) -> None: - """ - 推断任务后台执行器（真实实现版）。 - - OOM 防护策略: - - xr.open_zarr(..., chunks="auto") 延迟打开, 整图不一次性读入内存 - - xr.apply_ufunc(..., dask="parallelized") 把影像按 chunk 切分 - - 每个 chunk 内部 reshape 成 2D, 调 model.predict, 再 reshape 回 2D - - 任意时刻内存峰值 ≈ 1 个 (y_chunk, x_chunk, band) chunk 的体量 - - 整图完成计算后再 to_zarr(compute=True) 流式写出 - """ - record = await get_task(task_id) - if record is None: - print(f"[{task_id}] 推断任务不存在, 跳过") - return - - # 1. 校验 model_id 是否已注册 (避免在后台任务里报模糊错误) - model_meta = _MODEL_REGISTRY.get(model_id) - if model_meta is None: - await update_task( - task_id, - status="FAILED", - error=f"model_id 不存在: {model_id}", - updated_at=datetime.now().isoformat(), - ) - print(f"[{task_id}] 推断失败 -> model_id 不存在: {model_id}") - return - - # 2. 自动生成 output_zarr_path (若未提供) - if output_zarr_path is None: - stem = input_zarr_path.rstrip("/\\").split("/")[-1].split("\\")[-1] - stem = stem.replace(".zarr", "") - output_zarr_path = f"./data/{model_id}_{stem}_pred.zarr" - - await update_task( - task_id, - status="PROCESSING", - updated_at=datetime.now().isoformat(), - ) - print(f"[{task_id}] 开始推断 model_id={model_id} input={input_zarr_path}") - - try: - # 3. 探测波段数 (用于 Dummy 模型适配) - # 这里只读 zarr 元数据 (.zarray 的 shape), 不读真实数据 - ds_probe = xr.open_zarr(input_zarr_path, chunks="auto") - if "reflectance" not in ds_probe.data_vars: - raise KeyError( - f"输入 zarr 缺少 'reflectance' 变量; 实际: {list(ds_probe.data_vars)}" - ) - n_bands = ds_probe["reflectance"].sizes["band"] - ds_probe.close() - - # 4. 加载模型 (真实文件优先, Dummy 兜底) - model = _load_model(model_meta["path"], n_features=n_bands) - - # 5. 包装同步执行, 丢到线程池, 事件循环不阻塞 - await asyncio.to_thread( - _run_predict_sync, - model, - model_id, - input_zarr_path, - output_zarr_path, - ) - - await update_task( - task_id, - status="SUCCESS", - output_zarr_path=output_zarr_path, - model_id=model_id, - error=None, - updated_at=datetime.now().isoformat(), - ) - print(f"[{task_id}] 推断完成 -> output={output_zarr_path}") - - except Exception as exc: # noqa: BLE001 - tb_text = traceback.format_exc() - await update_task( - task_id, - status="FAILED", - output_zarr_path=None, - error=f"{type(exc).__name__}: {exc}", - traceback=tb_text, - updated_at=datetime.now().isoformat(), - ) - print(f"[{task_id}] 推断失败 -> {type(exc).__name__}: {exc}") - print(tb_text) - - -# --------------------------------------------------------------------------- -# POST /api/modeling/train -# --------------------------------------------------------------------------- -@router.post("/train", response_model=TaskAcceptedResponse) -async def submit_train( - payload: TrainRequest, - background_tasks: BackgroundTasks, -) -> Dict[str, Any]: - """提交一个模型训练任务, 立即返回 task_id。""" - - task_id = str(uuid.uuid4()) - await set_task( - task_id, - { - "task_id": task_id, - "kind": "train", - "model_type": payload.model_type, - "target": payload.target, - "train_data_path": payload.train_data_path, - "feature_start": payload.feature_start, - "params": payload.params, - "status": "PENDING", - "model_id": None, - "model_path": None, - "test_r2": None, - "test_rmse": None, - "test_mae": None, - "n_features": None, - "n_samples": None, - "error": None, - "traceback": None, - "created_at": datetime.now().isoformat(), - "updated_at": datetime.now().isoformat(), - }, - ) - background_tasks.add_task( - execute_train_task, - task_id, - payload.model_type, - payload.target, - payload.train_data_path, - payload.feature_start, - payload.params, - ) - return {"task_id": task_id, "status": "PENDING", "kind": "train"} - - -# --------------------------------------------------------------------------- -# GET /api/modeling/models -# --------------------------------------------------------------------------- -@router.get("/models", response_model=ModelListResponse) -async def list_trained_models() -> Dict[str, Any]: - """ - 列出已训练好的模型。 - - 未来实现: 从 ./data/models/*.joblib 扫描元信息, - 当前直接从内存 _MODEL_REGISTRY 读。 - """ - models = list(_MODEL_REGISTRY.values()) - # 按 created_at 倒序, 最新训练的在前 - models.sort(key=lambda m: m.get("created_at", ""), reverse=True) - return {"models": models, "count": len(models)} - - -# --------------------------------------------------------------------------- -# POST /api/modeling/predict -# --------------------------------------------------------------------------- -@router.post("/predict", response_model=TaskAcceptedResponse) -async def submit_predict( - payload: PredictRequest, - background_tasks: BackgroundTasks, -) -> Dict[str, Any]: - """提交一个模型推断任务, 立即返回 task_id。""" - - task_id = str(uuid.uuid4()) - await set_task( - task_id, - { - "task_id": task_id, - "kind": "predict", - "model_id": payload.model_id, - "input_zarr_path": payload.input_zarr_path, - "output_zarr_path": payload.output_zarr_path, - "status": "PENDING", - "error": None, - "traceback": None, - "created_at": datetime.now().isoformat(), - "updated_at": datetime.now().isoformat(), - }, - ) - background_tasks.add_task( - execute_predict_task, - task_id, - payload.model_id, - payload.input_zarr_path, - payload.output_zarr_path, - ) - return {"task_id": task_id, "status": "PENDING", "kind": "predict"} - - -# --------------------------------------------------------------------------- -# models_router — 独立于 modeling_router，路径前缀为 /models -# 最终完整路径: GET /api/models, POST /api/models/upload -# --------------------------------------------------------------------------- -models_router = APIRouter(prefix="/models", tags=["models"]) - - -# --------------------------------------------------------------------------- -# GET /api/models -# --------------------------------------------------------------------------- -@models_router.get("") -async def list_models() -> Dict[str, Any]: - """ - 扫描 ./data/models/ 目录，返回所有 .joblib 文件名（不含后缀）。 - - 异常处理：目录不存在时自动创建，返回空列表。 - """ - models_dir = Path("./data/models") - models_dir.mkdir(parents=True, exist_ok=True) - - model_names = [ - p.stem for p in models_dir.iterdir() if p.suffix == ".joblib" - ] - return {"models": model_names} - - -# --------------------------------------------------------------------------- -# POST /api/models/upload -# --------------------------------------------------------------------------- -@models_router.post("/upload") -async def upload_model( - file: UploadFile = File(...), -) -> Dict[str, Any]: - """ - 接收上传的 .joblib 模型文件，保存到 ./data/models/ 目录。 - - - 校验后缀必须为 .joblib - - 目录不存在时自动创建 - - 返回状态和文件名（不含后缀） - """ - if not file.filename or not file.filename.lower().endswith(".joblib"): - raise HTTPException( - status_code=400, - detail="仅支持 .joblib 格式的文件", - ) - - models_dir = Path("./data/models") - models_dir.mkdir(parents=True, exist_ok=True) - - dest_path = models_dir / file.filename - - with dest_path.open("wb") as buffer: - shutil.copyfileobj(file.file, buffer) - - return { - "status": "success", - "model_id": dest_path.stem, - } diff --git a/new/app/core/algorithms/__init__.py b/new/app/core/algorithms/__init__.py deleted file mode 100644 index 6cd03b7..0000000 --- a/new/app/core/algorithms/__init__.py +++ /dev/null @@ -1,40 +0,0 @@ -""" -去耀斑算法包 -============ - -通过「注册表 + 策略模式」组织不同的去耀斑算法。 -所有具体算法都应继承 BaseGlintRemover，并使用 @register_glint_remover -装饰器把算法名和实现类绑定。 - -外部调用约定 ------------- -1. 所有算法子模块必须在本 __init__ 中显式 import， - 这样装饰器才会被执行、注册表才会被填满。 -2. 上层（endpoints、worker）只允许： - from app.core.algorithms import get_remover - 来获取算法类，不要直接 import 具体实现类， - 保持调度层与具体算法的解耦。 -""" - -from app.core.algorithms.base import BaseGlintRemover -from app.core.algorithms.registry import ( - get_remover, - list_removers, - register_glint_remover, - unregister_glint_remover, -) - -# ---- 算法子模块 import 区 ---- -# 新增算法时，在这里加一行 import，确保装饰器被执行。 -from app.core.algorithms import goodman # Goodman -from app.core.algorithms import kutser # Kutser -# from app.core.algorithms import hedley # Hedley -# from app.core.algorithms import sugar # SUGAR - -__all__ = [ - "BaseGlintRemover", - "register_glint_remover", - "get_remover", - "list_removers", - "unregister_glint_remover", -] diff --git a/new/app/core/algorithms/base.py b/new/app/core/algorithms/base.py deleted file mode 100644 index 47bce25..0000000 --- a/new/app/core/algorithms/base.py +++ /dev/null @@ -1,85 +0,0 @@ -""" -去耀斑算法抽象基类 -================== - -设计目标（策略模式 Strategy Pattern） ------------------------------------- -本模块定义了所有去耀斑算法必须遵守的标准接口。 -未来的 Kutser、Goodman、Hedley、SUGAR 等算法都将继承本基类， -并实现统一的 process() 方法。 - -输入输出规范 ------------- -所有算法的输入与输出均统一为 **Zarr 文件路径**（字符串）， -而不是内存中的 numpy ndarray。这样做的核心收益是： - - 1. **解耦数据存储与内存计算**： - 算法只关心「从哪个 zarr 读、写到哪个 zarr」， - 至于数据最初来自 GeoTIFF / HDF5 / NetCDF / 内存数组， - 都由 IO 层负责归一化转为 zarr。 - 2. **支持 Out-of-Core 计算**： - 影像往往超过内存上限，zarr 分块（chunk）天然支持按块读取， - 算法实现可以借助 dask / xarray 进行流式计算。 - 3. **可缓存、可复用**： - 中间产物落盘后，下游算法（大气校正、辐射定标）能直接消费， - 避免重复 IO。 - 4. **易于并行与分布式**： - 任务调度层只需把两个路径扔给 worker，无需关心数据细节。 - -约定 ----- -- 子类应实现 process()，完成「读 -> 计算 -> 写」的完整流程。 -- process() 返回 True 表示成功，False 表示失败。 -- 失败时建议抛出异常而非仅返回 False，便于上层 BackgroundTasks 捕获并写入 error 字段。 -""" - -from abc import ABC, abstractmethod -from typing import Any - - -class BaseGlintRemover(ABC): - """ - 去耀斑算法抽象基类。 - - 所有具体算法（Kutser / Goodman / Hedley / SUGAR …）必须继承本类并实现 process()。 - 子类可在 __init__ 中接收自己的超参数（如参考波段、阈值等）， - 真正的输入输出数据则由 process() 的两个 zarr 路径参数指定。 - """ - - # 子类可覆盖的算法名称标识，用于调度层按 method 名字查找 - name: str = "base" - - @abstractmethod - async def process( - self, - input_zarr_path: str, - output_zarr_path: str, - **kwargs: Any, - ) -> bool: - """ - 执行去耀斑处理。 - - Parameters - ---------- - input_zarr_path : str - 输入高光谱影像的 zarr 存储路径。 - 数据已由 IO 层完成格式归一化（波段、坐标系、空间维度均已对齐）。 - output_zarr_path : str - 处理结果（去耀斑后影像）的 zarr 存储路径。 - 子类需自行创建该 zarr 存储并写入结果。 - **kwargs : Any - 算法的可选超参数，例如： - - reference_band: 参考近红外波段索引 - - chunk_size: 计算分块大小 - - 其它算法特定参数 - - Returns - ------- - bool - True 表示处理成功，False 表示失败。 - 建议在出错时直接 raise，由调用方统一记录到任务状态。 - """ - raise NotImplementedError - - def __repr__(self) -> str: # pragma: no cover - 调试辅助 - return f"<{self.__class__.__name__} name={self.name!r}>" diff --git a/new/app/core/algorithms/goodman.py b/new/app/core/algorithms/goodman.py deleted file mode 100644 index c6f3ecf..0000000 --- a/new/app/core/algorithms/goodman.py +++ /dev/null @@ -1,123 +0,0 @@ -""" -app/core/algorithms/goodman.py -=============================== - -Goodman et al. 2008 去耀斑算法的 xarray + dask 流式实现。 - -算法公式 --------- - R_corrected = R_raw - R_750 + A + B * (R_640 - R_750) - -其中： - R_raw -- 原始反射率 (y, x, band) - R_750 -- λ=750 nm 处的反射率（红外参考波段, 远离水汽吸收） - R_640 -- λ=640 nm 处的反射率（可见光差异波段） - A, B -- 经验回归参数（用户可通过 params 传入, 默认全 0） - -后处理 ------- -- 负值截断为 0（Clamp to 0） -- 仅在水域掩膜 (water_mask) 内生效, 水外置 0 - -维度约定 --------- - reflectance: (y, x, band), band 坐标通常为 wavelength (nm) - water_mask : (y, x), 布尔类型, True = 水域 -""" - -import asyncio -from typing import Any - -import xarray as xr - -from app.core.algorithms.base import BaseGlintRemover -from app.core.algorithms.registry import register_glint_remover - - -# --------------------------------------------------------------------------- -# 默认参数 -# --------------------------------------------------------------------------- -# 与原始 Goodman 2008 论文符号保持一致, 方便用户交叉对照。 -# A、B 通常通过对纯净深水区做 (R_corr - R_raw) ~ (R_640 - R_750) 回归得到; -# 在缺乏先验知识时, 退化为 A=0, B=0 即等价于 R_corrected = clip(R_raw - R_750, 0)。 -# --------------------------------------------------------------------------- -DEFAULT_BAND_REF: float = 750.0 # λ_750 nm, 红外参考波段 -DEFAULT_BAND_DIFF: float = 640.0 # λ_640 nm, 可见光差异波段 -DEFAULT_A: float = 0.0 # 公式中的常数偏移项 -DEFAULT_B: float = 0.0 # 公式中的斜率项 - - -@register_glint_remover("goodman") -class GoodmanGlintRemover(BaseGlintRemover): - """Goodman et al. 2008 去耀斑算法""" - - name = "goodman" - - async def process( - self, - input_zarr_path: str, - output_zarr_path: str, - **kwargs: Any, - ) -> bool: - # 1. 解析超参数（带默认值, 方便用户按需覆盖） - band_ref: float = kwargs.get("band_ref", DEFAULT_BAND_REF) - band_diff: float = kwargs.get("band_diff", DEFAULT_BAND_DIFF) - A: float = kwargs.get("A", DEFAULT_A) - B: float = kwargs.get("B", DEFAULT_B) - - # 2. 把同步的 xarray/dask 计算丢到工作线程, - # 避免阻塞 FastAPI 的事件循环 - return await asyncio.to_thread( - self._process_sync, - input_zarr_path, - output_zarr_path, - band_ref, - band_diff, - A, - B, - ) - - @staticmethod - def _process_sync( - input_zarr_path: str, - output_zarr_path: str, - band_ref: float, - band_diff: float, - A: float, - B: float, - ) -> bool: - # 1. 以 zarr 路径打开（dask-backed, 不物化到内存） - # chunks="auto" 让 dask 根据每条坐标轴的大小自动决定分块 - ds = xr.open_zarr(input_zarr_path, chunks="auto") - reflectance = ds["reflectance"] # (y, x, band) - - # 2. 用 sel + method='nearest' 提取两个关键波段 - # 返回形状 (y, x), 后续与 (y, x, band) 算术时会自动广播 - R_750 = reflectance.sel(band=band_ref, method="nearest") - R_640 = reflectance.sel(band=band_diff, method="nearest") - - # 3. Goodman 公式: xarray 沿 band 维度自动广播 - # R_corr = R_raw - R_750 + A + B * (R_640 - R_750) - result = reflectance - R_750 + A + B * (R_640 - R_750) - - # 4. 负值截断为 0（clip(min=0) 优于 where(>0, 0, _)： - # 不构造布尔中间数组, 底层走 dask 矢量化 clip 路径） - result = result.clip(min=0) - - # 5. 仅在水域内生效（水外强制为 0） - # 优先从 zarr 内部读 water_mask 变量, 缺失则视为全图水域 - if "water_mask" in ds: - water_mask = ds["water_mask"].astype(bool) - result = result.where(water_mask, 0) - - # 6. 构造输出 Dataset, 保留元信息（波段坐标/属性等） - out = xr.Dataset({"reflectance": result}) - if ds.attrs: - out.attrs = dict(ds.attrs) - if reflectance.attrs: - out["reflectance"].attrs = dict(reflectance.attrs) - - # 7. 流式写出（Out-of-Core）：不一次性物化大数组, - # dask 会按 chunk 边算边写, 内存峰值 ≈ 单个 chunk 大小 - out.to_zarr(output_zarr_path, mode="w", compute=True) - return True diff --git a/new/app/core/algorithms/kutser.py b/new/app/core/algorithms/kutser.py deleted file mode 100644 index 73aff2f..0000000 --- a/new/app/core/algorithms/kutser.py +++ /dev/null @@ -1,211 +0,0 @@ -""" -Kutser 去耀斑算法（xarray + dask 重构版） -======================================== - -旧版痛点 --------- -原始 Kutser 实现（参考 Kutser et al., 2013）通常写成像这样： - - R_corr = np.zeros_like(R_raw) - for b in range(n_bands): - for y in range(H): - for x in range(W): - if water_mask[y, x]: - R_corr[y, x, b] = ( - R_raw[y, x, b] - G_list[b] * D_norm[y, x] - ) - with rasterio.open(..., 'w') as dst: - dst.write(R_corr) - -问题： - 1. 三重 Python 循环，每次只做一个浮点运算，解释器开销巨大； - 2. 一次性把整张图 R_raw 读进内存，大影像直接 OOM； - 3. rasterio 写出要求 numpy 连续数组，进一步放大内存。 - -本文件用 xarray + dask 重写： - - 用 DataArray 维度广播，三重循环 → 一行表达式； - - 用 dask chunk 保持数据常驻磁盘、流式计算； - - 用 to_zarr 边算边写，输出格式与算法层彻底解耦。 -""" - -import asyncio -from typing import Any - -import xarray as xr - -from app.core.algorithms.base import BaseGlintRemover -from app.core.algorithms.registry import register_glint_remover - - -# --------------------------------------------------------------------------- -# 算法实现 -# --------------------------------------------------------------------------- -@register_glint_remover("kutser") -class KutserGlintRemover(BaseGlintRemover): - """ - Kutser 近红外扣除法去耀斑。 - - 数学公式（与旧版完全等价） - ------------------------- - 1) 水汽吸收深度 D（每像素）： - D = (R(λ_lower) + R(λ_upper)) / 2 - R(λ_oxy) - 2) 全局归一化因子 D_max： - D_max = max(D) over 水域 - 归一化： - D_norm = D / D_max - 3) 每波段水域范围： - G_list[b] = max(R[:, :, b] over 水域) - min(R[:, :, b] over 水域) - 4) 校正公式（每像素、每波段）： - R_corr(λ_b) = R_raw(λ_b) - G_list[b] * D_norm - """ - - # Kutser 2013 论文里使用的参考波段（nm）： - # λ_lower = 773, λ_oxy = 845, λ_upper = 893 - # 允许通过 kwargs 覆盖，便于适配 MERIS / OLCI / Landsat 等不同传感器。 - DEFAULT_BAND_LOWER: float = 773.0 - DEFAULT_BAND_OXY: float = 845.0 - DEFAULT_BAND_UPPER: float = 893.0 - - # -------------------------------------------------------------- - # 公开异步入口 - # -------------------------------------------------------------- - # xarray / dask 的算子本身是同步阻塞的。在 async 函数中， - # 用 asyncio.to_thread 把同步体丢到默认线程池执行， - # 避免阻塞 FastAPI 的事件循环。 - # -------------------------------------------------------------- - async def process( - self, - input_zarr_path: str, - output_zarr_path: str, - **kwargs: Any, - ) -> bool: - return await asyncio.to_thread( - self._process_sync, - input_zarr_path, - output_zarr_path, - kwargs, - ) - - # -------------------------------------------------------------- - # 同步核心实现 - # -------------------------------------------------------------- - def _process_sync( - self, - input_zarr_path: str, - output_zarr_path: str, - kwargs: dict, - ) -> bool: - # ============================================================ - # 步骤 0：打开 zarr，建立 dask 计算图 - # ============================================================ - # chunks="auto"：让 dask 根据 zarr 的存储分块自动选择内存上限， - # 数据不会一次性全部 materialize 进 RAM。 - # ============================================================ - ds = xr.open_zarr(input_zarr_path, chunks="auto") - reflectance: xr.DataArray = ds["reflectance"] # 维度约定：(y, x, band) - - # 维度顺序约定（也可根据 ds.dims 自动适配）： - assert "y" in reflectance.dims and "x" in reflectance.dims and "band" in reflectance.dims, ( - f"reflectance 必须包含 y/x/band 三个维度，实际为: {reflectance.dims}" - ) - - # ============================================================ - # 步骤 1：取出 3 个参考波段对应的二维 (y, x) 切片 - # ============================================================ - # 假设 band 维度的坐标是 wavelength（nm）。 - # 用 sel(..., method="nearest") 自动匹配最接近的波段。 - # ============================================================ - wl_lower = float(kwargs.get("band_lower", self.DEFAULT_BAND_LOWER)) - wl_oxy = float(kwargs.get("band_oxy", self.DEFAULT_BAND_OXY)) - wl_upper = float(kwargs.get("band_upper", self.DEFAULT_BAND_UPPER)) - - R_lower = reflectance.sel(band=wl_lower, method="nearest") # (y, x) - R_upper = reflectance.sel(band=wl_upper, method="nearest") # (y, x) - R_oxy = reflectance.sel(band=wl_oxy, method="nearest") # (y, x) - - # ============================================================ - # 步骤 2：水域掩膜 - # ============================================================ - # 优先从 zarr 内部读取 water_mask 变量； - # 如果不存在，则假定整幅图都是水域（开发期兜底）。 - # ============================================================ - if "water_mask" in ds: - water_mask = ds["water_mask"].astype(bool) - else: - water_mask = xr.ones_like( - reflectance.isel(band=0), dtype=bool - ) - - # ============================================================ - # 步骤 3：水汽吸收深度 D（每像素，形状 (y, x)） - # ============================================================ - # 旧版：D[y, x] = (R_lower[y, x] + R_upper[y, x]) / 2 - R_oxy[y, x] - # 新版：一行表达式，dask 自动构建 lazy 计算图。 - # ============================================================ - D = (R_lower + R_upper) / 2.0 - R_oxy # (y, x)，dtype 与 reflectance 一致 - - # ============================================================ - # 步骤 4：全局归一化因子 D_max（标量，0-dim DataArray） - # ============================================================ - # 关键：先 .where(water_mask) 把非水域置 NaN， - # 再 .max() 跨 (x, y) 聚合，自动规约到 0 维。 - # dask 此时仍然没有真正计算，等到 to_zarr 时再触发。 - # ============================================================ - D_max = D.where(water_mask).max() # scalar - # 容错：如果水域为空导致 D_max 为 NaN，用极小值兜底，避免除零 - D_max = D_max.fillna(1e-6) - - # ============================================================ - # 步骤 5：归一化 D_norm（形状 (y, x)） - # ============================================================ - D_norm = D / D_max # 标量除以 (y, x) 数组 → 自动广播 - - # ============================================================ - # 步骤 6：每波段水域范围 G_list（形状 (band,)） - # ============================================================ - # 旧版三重循环内部还要做一次 min/max 聚合。 - # xarray 版本：把 (y, x) 一起 reduce，只保留 band 维度。 - # ============================================================ - R_water = reflectance.where(water_mask) # (y, x, band)，非水域 NaN - G_min = R_water.min(dim=["x", "y"]) # (band,) - G_max = R_water.max(dim=["x", "y"]) # (band,) - G_list = (G_max - G_min).fillna(0.0) # (band,)，容错 - - # ============================================================ - # 步骤 7：校正公式（最关键的一行，演示 xarray 广播） - # ============================================================ - # 旧版需要： - # for b in bands: - # for y in range(H): - # for x in range(W): - # R_corr[y,x,b] = R_raw[y,x,b] - G_list[b] * D_norm[y,x] - # - # xarray 维度对齐规则： - # R_raw : (y, x, band) - # G_list: (band,) → 缺失 y, x 自动扩展 - # D_norm: (y, x) → 缺失 band 自动扩展 - # 乘法结果: (y, x, band) → 减法对齐 - # 一行表达式完成「三重 for 循环 + 标量索引」的语义。 - # ============================================================ - corrected = reflectance - G_list * D_norm # (y, x, band) - - # ============================================================ - # 步骤 8：水域掩膜过滤（非水域置 NaN） - # ============================================================ - result = corrected.where(water_mask) - - # ============================================================ - # 步骤 9：持久化为 zarr - # ============================================================ - # mode="w"：覆盖写入（如果目标已存在则删除重建）。 - # compute=True：阻塞直到整张图算完并落盘。 - # 由于数据始终是 dask chunk + 流式写出， - # 内存峰值 ≈ 单个 chunk 大小，与整张影像大小无关。 - # ============================================================ - out = xr.Dataset({"reflectance": result}) - # 保留原数据集的全局属性 / 坐标信息（CRS、wavelength、...） - out.attrs = dict(ds.attrs) - out["reflectance"].attrs = dict(reflectance.attrs) - out.to_zarr(output_zarr_path, mode="w", compute=True) - - return True diff --git a/new/app/core/algorithms/registry.py b/new/app/core/algorithms/registry.py deleted file mode 100644 index d70e841..0000000 --- a/new/app/core/algorithms/registry.py +++ /dev/null @@ -1,135 +0,0 @@ -""" -算法注册表（Registry / Factory） -================================ - -通过装饰器把「算法名字符串」与「算法实现类」绑定在一起。 -上层调度层（FastAPI endpoints、BackgroundTasks worker）只需要拿到 -前端传过来的 method 字符串，就可以自动派发到对应的算法实现， -而无需写一长串 if/elif。 - -使用示例 --------- - - from app.core.algorithms import BaseGlintRemover - from app.core.algorithms.registry import ( - register_glint_remover, - get_remover, - list_removers, - ) - - @register_glint_remover("kutser") - class KutserGlintRemover(BaseGlintRemover): - async def process(self, input_zarr_path, output_zarr_path, **kwargs): - ... - - # 派发 - Cls = get_remover(method_from_request) - remover = Cls() - await remover.process(input_zarr_path, output_zarr_path, **kwargs) - -设计要点 --------- -- 注册动作发生在「类定义时」，所以必须在所有算法 import 完之后 - 注册表才完整。可以在 `app/core/algorithms/__init__.py` 中 - 把算法子模块 import 一遍来强制触发注册。 -- 重复注册同名算法会直接抛错，避免静默覆盖。 -- name 会同步写回到类的 `name` 属性，便于算法自身查询身份。 -""" - -from typing import Dict, Type - -from app.core.algorithms.base import BaseGlintRemover - - -# 全局注册表：name(str) -> 实现类(type)，类未被实例化 -_REGISTRY: Dict[str, Type[BaseGlintRemover]] = {} - - -def register_glint_remover(name: str): - """ - 类装饰器工厂：把传入 name 的算法类注册到全局注册表。 - - Parameters - ---------- - name : str - 算法标识，建议小写下划线风格，例如 "kutser"、"goodman"。 - - Raises - ------ - ValueError - - name 不是非空字符串 - - name 已经被其它类占用 - TypeError - - 被装饰的对象不是 BaseGlintRemover 的子类 - """ - - # ---- 防御性校验：name 必须是合法字符串 ---- - if not isinstance(name, str) or not name.strip(): - raise ValueError( - f"register_glint_remover 的 name 必须是非空字符串，收到: {name!r}" - ) - - def decorator(cls: Type[BaseGlintRemover]) -> Type[BaseGlintRemover]: - # ---- 防御性校验：被装饰对象必须是 BaseGlintRemover 子类 ---- - if not isinstance(cls, type) or not issubclass(cls, BaseGlintRemover): - raise TypeError( - f"@register_glint_remover 只能装饰 BaseGlintRemover 的子类，" - f"收到: {cls!r}" - ) - - # ---- 防御性校验：禁止静默覆盖 ---- - if name in _REGISTRY: - raise ValueError( - f"算法名 {name!r} 已被 {_REGISTRY[name].__name__} 占用，" - f"请使用其它名字或先调用 unregister_glint_remover() 注销旧实现。" - ) - - # 同步把 name 写回类属性，便于算法自身和日志输出使用 - cls.name = name - _REGISTRY[name] = cls - return cls - - return decorator - - -def get_remover(name: str) -> Type[BaseGlintRemover]: - """ - 按算法名字符串取出对应的实现类（未实例化）。 - - 调用方拿到类后自行 `Cls(...)` 构造实例，再调用 process()。 - - Raises - ------ - KeyError - 当 name 不在注册表中时抛出，错误信息中附带已注册列表便于排查。 - """ - try: - return _REGISTRY[name] - except KeyError as exc: - known = ", ".join(sorted(_REGISTRY)) or "<空>" - raise KeyError( - f"未注册的算法名: {name!r}。已注册的算法: {known}" - ) from exc - - -def list_removers() -> Dict[str, Type[BaseGlintRemover]]: - """ - 返回当前注册表的浅拷贝。 - 可用于： - - 调试日志 - - 给前端暴露一个 GET /api/algorithms 接口 - - 单元测试断言 - """ - return dict(_REGISTRY) - - -def unregister_glint_remover(name: str) -> None: - """ - 注销指定算法。主要给： - - 单元测试 - - 热重载 / 插件卸载场景 - 生产代码一般不需要调用。 - """ - if name not in _REGISTRY: - raise KeyError(f"未注册的算法名: {name!r}") - del _REGISTRY[name] diff --git a/new/app/core/task_store.py b/new/app/core/task_store.py deleted file mode 100644 index aa1545d..0000000 --- a/new/app/core/task_store.py +++ /dev/null @@ -1,91 +0,0 @@ -""" -app/core/task_store.py -====================== - -并发安全的内存任务状态存储，替代早期 mock 流水线中的 MOCK_TASK_DB。 - -设计目标 --------- -1. 在单进程内提供事件循环级别的互斥（asyncio.Lock）， - 避免在 update 与 set/get 之间穿插 await 时发生状态不一致。 -2. 暴露异步 API（set_task / update_task / get_task）， - 让调用方在 async 上下文中显式表达临界区。 -3. 保留一个同步的 has_task() 用于轻量存在性判断。 -4. 生产环境应替换为 Redis / SQLite / PostgreSQL， - 但接口形状保持一致, 便于上层调用方无缝迁移。 - -使用约定 --------- -- 写入初始 PENDING 记录： await set_task(task_id, record) -- 增量更新字段（PROCESSING/SUCCESS/FAILED）：await update_task(task_id, **fields) -- 读取任务记录： await get_task(task_id) # 可能返回 None -- 同步判断是否存在： has_task(task_id) -""" - -import asyncio -from typing import Any, Dict, Optional - - -# --------------------------------------------------------------------------- -# 全局存储与锁 -# --------------------------------------------------------------------------- -# TASK_STORE: task_id -> 任务记录 -# 任务记录字段约定（与 endpoints.py 保持一致）： -# task_id, method, params, status, -# output_zarr_path, error, traceback, -# created_at, updated_at -# --------------------------------------------------------------------------- -TASK_STORE: Dict[str, Dict[str, Any]] = {} - -# 单进程内的事件循环级互斥锁 -# 注意：asyncio.Lock 必须在事件循环内创建, 故在模块顶层实例化时 -# 仅获取引用, 第一次使用 (await lock.acquire()) 会在运行循环内进行。 -_lock: asyncio.Lock = asyncio.Lock() - - -# --------------------------------------------------------------------------- -# 异步 API -# --------------------------------------------------------------------------- -async def set_task(task_id: str, record: Dict[str, Any]) -> None: - """ - 初始化或整体覆盖一个任务记录。 - - 用法：POST 端点收到提交请求后立即调用, 写入 PENDING 状态的初始记录。 - """ - async with _lock: - TASK_STORE[task_id] = record - - -async def update_task(task_id: str, **fields: Any) -> None: - """ - 按字段增量更新任务记录。 - - 用法：后台执行器在 PROCESSING / SUCCESS / FAILED 等状态切换时调用。 - 若 task_id 不存在, setdefault 会自动创建一个空 dict 再 update（防御性兜底）。 - """ - async with _lock: - record = TASK_STORE.setdefault(task_id, {}) - record.update(fields) - - -async def get_task(task_id: str) -> Optional[Dict[str, Any]]: - """ - 读取任务记录; 不存在时返回 None。 - - 用法：GET /api/tasks/{task_id} 用此接口查询。 - """ - async with _lock: - return TASK_STORE.get(task_id) - - -# --------------------------------------------------------------------------- -# 同步 API（轻量） -# --------------------------------------------------------------------------- -def has_task(task_id: str) -> bool: - """ - 同步判断 task_id 是否存在。 - - 适用于不需要锁的轻量场景（例如日志前置判断）; - 在 async 上下文中仍可调用, 因为 dict 的 in 判断是原子操作。 - """ - return task_id in TASK_STORE diff --git a/new/app/main.py b/new/app/main.py deleted file mode 100644 index 789ade3..0000000 --- a/new/app/main.py +++ /dev/null @@ -1,64 +0,0 @@ -""" -WQ_GUI FastAPI 后端入口 -======================= - -应用启动与全局中间件配置： - - CORS：开发阶段允许所有来源，方便本地前端（Vite / Webpack dev server）联调 - - 路由：通过 include_router 挂载 app/api/endpoints.py 中的业务接口 - -业务接口说明： - POST /api/process/deglint 提交去耀斑处理任务，立即返回 task_id - GET /api/tasks/{task_id} 查询指定任务的状态与结果 -""" - -from typing import Dict - -from fastapi import FastAPI -from fastapi.middleware.cors import CORSMiddleware - -from app.api.endpoints import router as deglint_router -from app.api.modeling import router as modeling_router -from app.api.modeling import models_router - - -# --------------------------------------------------------------------------- -# FastAPI 应用实例 -# --------------------------------------------------------------------------- -app = FastAPI( - title="WQ_GUI Backend", - description="高光谱影像去耀斑处理 API", - version="0.2.0", -) - - -# --------------------------------------------------------------------------- -# CORS 中间件 -# --------------------------------------------------------------------------- -# 开发阶段：放开所有来源、方法和头部，方便本地前端（任意端口）联调。 -# 生产环境务必收敛 allow_origins 为前端真实域名，避免安全风险。 -# --------------------------------------------------------------------------- -app.add_middleware( - CORSMiddleware, - allow_origins=["*"], - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], -) - - -# --------------------------------------------------------------------------- -# 路由注册 -# --------------------------------------------------------------------------- -# 统一以 /api 为前缀，便于将来做版本管理（如 /api/v1、/api/v2）。 -# --------------------------------------------------------------------------- -app.include_router(deglint_router, prefix="/api") -app.include_router(modeling_router, prefix="/api") -app.include_router(models_router, prefix="/api") - - -# --------------------------------------------------------------------------- -# 根路径健康检查（方便本地调试，非业务必需） -# --------------------------------------------------------------------------- -@app.get("/") -async def root() -> Dict[str, str]: - return {"service": "WQ_GUI Backend", "status": "ok"} diff --git a/new/frontend/.gitignore b/new/frontend/.gitignore deleted file mode 100644 index a547bf3..0000000 --- a/new/frontend/.gitignore +++ /dev/null @@ -1,24 +0,0 @@ -# Logs -logs -*.log -npm-debug.log* -yarn-debug.log* -yarn-error.log* -pnpm-debug.log* -lerna-debug.log* - -node_modules -dist -dist-ssr -*.local - -# Editor directories and files -.vscode/* -!.vscode/extensions.json -.idea -.DS_Store -*.suo -*.ntvs* -*.njsproj -*.sln -*.sw? diff --git a/new/frontend/README.md b/new/frontend/README.md deleted file mode 100644 index 33895ab..0000000 --- a/new/frontend/README.md +++ /dev/null @@ -1,5 +0,0 @@ -# Vue 3 + TypeScript + Vite - -This template should help get you started developing with Vue 3 and TypeScript in Vite. 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a/new/frontend/public/favicon.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/new/frontend/public/icons.svg b/new/frontend/public/icons.svg deleted file mode 100644 index e952219..0000000 --- a/new/frontend/public/icons.svg +++ /dev/null @@ -1,24 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/new/frontend/src/App.vue b/new/frontend/src/App.vue deleted file mode 100644 index e5db726..0000000 --- a/new/frontend/src/App.vue +++ /dev/null @@ -1,225 +0,0 @@ - - - - - - - diff --git a/new/frontend/src/api/request.ts b/new/frontend/src/api/request.ts deleted file mode 100644 index 368e9f2..0000000 --- a/new/frontend/src/api/request.ts +++ /dev/null @@ -1,15 +0,0 @@ -import axios from 'axios' - -const request = axios.create({ - // 注意：直接指向我们刚刚改好的 9090 端口 - baseURL: 'http://127.0.0.1:9090', - timeout: 60000 -}) - -// 拦截器：直接剥离 data -request.interceptors.response.use( - response => response.data, - error => Promise.reject(error) -) - -export default request diff --git a/new/frontend/src/api/tasks.ts b/new/frontend/src/api/tasks.ts deleted file mode 100644 index e9e93f7..0000000 --- a/new/frontend/src/api/tasks.ts +++ /dev/null @@ -1,13 +0,0 @@ -import request from './request' - -export const submitTrain = (data: any) => { - return request.post('/api/modeling/train', data) -} - -export const submitPredict = (data: any) => { - return request.post('/api/modeling/predict', data) -} - -export const getTaskStatus = (task_id: string) => { - return request.get(`/api/tasks/${task_id}`) -} diff --git a/new/frontend/src/assets/hero.png b/new/frontend/src/assets/hero.png deleted file mode 100644 index 02251f4b956c55af2d76fd0788124d7eee2b45eb..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 13057 zcmV+cGycqpP)V|)f$;Qooc7=_G zlYe)HToTQIc!$)^+J1M1y0*T%w!p~7%ux`!eRhO?c80XDxKQ*R^lUUMnA>6NT^?feoZ8xxvP32D&s-9ow zqjcM}eesrC)NeDmsf)*P7wJ|K!&xP%Zy4iI8lF)Tv2!reW)tCzg_1=PmOwd1SQfxa 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a/new/frontend/src/components/HelloWorld.vue b/new/frontend/src/components/HelloWorld.vue deleted file mode 100644 index c232865..0000000 --- a/new/frontend/src/components/HelloWorld.vue +++ /dev/null @@ -1,95 +0,0 @@ - - - diff --git a/new/frontend/src/composables/useTaskPoller.ts b/new/frontend/src/composables/useTaskPoller.ts deleted file mode 100644 index ec47ea8..0000000 --- a/new/frontend/src/composables/useTaskPoller.ts +++ /dev/null @@ -1,51 +0,0 @@ -import { ref, watch, onUnmounted, type Ref } from 'vue' -import { getTaskStatus } from '../api/tasks' - -export function useTaskPoller(taskIdRef: Ref) { - const status = ref('') - const isPolling = ref(false) - const error = ref(null) - const result = ref(null) - let timer: any = null - - const start = () => { - if (!taskIdRef.value) return - isPolling.value = true - error.value = null - status.value = 'PENDING' - - timer = setInterval(async () => { - try { - const res = await getTaskStatus(taskIdRef.value!) - status.value = res.status - - if (res.status === 'SUCCESS') { - result.value = res - stop() - } else if (res.status === 'FAILED') { - error.value = res.error || '任务执行失败' - stop() - } - } catch (e: any) { - error.value = '网络请求失败，请检查后端状态' - stop() - } - }, 2000) - } - - const stop = () => { - isPolling.value = false - if (timer) clearInterval(timer) - } - - // 监听 Task ID 变化自动开启轮询 - watch(taskIdRef, (newVal) => { - stop() - if (newVal) start() - }) - - // 组件销毁时清理定时器 - onUnmounted(() => stop()) - - return { status, isPolling, error, result, stop } -} diff --git a/new/frontend/src/main.ts b/new/frontend/src/main.ts deleted file mode 100644 index 9489de8..0000000 --- a/new/frontend/src/main.ts +++ /dev/null @@ -1,9 +0,0 @@ -import { createApp } from 'vue' -import ElementPlus from 'element-plus' -import 'element-plus/dist/index.css' -import App from './App.vue' - -const app = createApp(App) - -app.use(ElementPlus) -app.mount('#app') diff --git a/new/frontend/src/style.css b/new/frontend/src/style.css deleted file mode 100644 index 527d4fb..0000000 --- a/new/frontend/src/style.css +++ /dev/null @@ -1,296 +0,0 @@ -:root { - --text: #6b6375; - --text-h: #08060d; - --bg: #fff; - --border: #e5e4e7; - --code-bg: #f4f3ec; - --accent: #aa3bff; - --accent-bg: rgba(170, 59, 255, 0.1); - --accent-border: rgba(170, 59, 255, 0.5); - --social-bg: rgba(244, 243, 236, 0.5); - --shadow: - rgba(0, 0, 0, 0.1) 0 10px 15px -3px, rgba(0, 0, 0, 0.05) 0 4px 6px -2px; - - --sans: system-ui, 'Segoe UI', Roboto, sans-serif; - --heading: system-ui, 'Segoe UI', Roboto, sans-serif; - --mono: ui-monospace, Consolas, monospace; - - font: 18px/145% var(--sans); - letter-spacing: 0.18px; - color-scheme: light dark; - color: var(--text); - background: var(--bg); - font-synthesis: none; - text-rendering: optimizeLegibility; - -webkit-font-smoothing: antialiased; - -moz-osx-font-smoothing: grayscale; - - @media (max-width: 1024px) { - font-size: 16px; - } -} - -@media (prefers-color-scheme: dark) { - :root { - --text: #9ca3af; - --text-h: #f3f4f6; - --bg: #16171d; - --border: #2e303a; - --code-bg: #1f2028; - --accent: #c084fc; - --accent-bg: rgba(192, 132, 252, 0.15); - --accent-border: rgba(192, 132, 252, 0.5); - --social-bg: rgba(47, 48, 58, 0.5); - --shadow: - rgba(0, 0, 0, 0.4) 0 10px 15px -3px, rgba(0, 0, 0, 0.25) 0 4px 6px -2px; - } - - #social .button-icon { - filter: invert(1) brightness(2); - } -} - -body { - margin: 0; -} - -h1, -h2 { - font-family: var(--heading); - font-weight: 500; - color: var(--text-h); -} - -h1 { - font-size: 56px; - letter-spacing: -1.68px; - margin: 32px 0; - @media (max-width: 1024px) { - font-size: 36px; - margin: 20px 0; - } -} -h2 { - font-size: 24px; - line-height: 118%; - letter-spacing: -0.24px; - margin: 0 0 8px; - @media (max-width: 1024px) { - font-size: 20px; - } -} -p { - margin: 0; -} - -code, -.counter { - font-family: var(--mono); - display: inline-flex; - border-radius: 4px; - color: var(--text-h); -} - -code { - font-size: 15px; - line-height: 135%; - padding: 4px 8px; - background: var(--code-bg); -} - -.counter { - font-size: 16px; - padding: 5px 10px; - border-radius: 5px; - color: var(--accent); - background: var(--accent-bg); - border: 2px solid transparent; - transition: border-color 0.3s; - margin-bottom: 24px; - - &:hover { - border-color: var(--accent-border); - } - &:focus-visible { - outline: 2px solid var(--accent); - outline-offset: 2px; - } -} - -.hero { - position: relative; - - .base, - .framework, - .vite { - inset-inline: 0; - margin: 0 auto; - } - - .base { - width: 170px; - position: relative; - z-index: 0; - } - - .framework, - .vite { - position: absolute; - } - - .framework { - z-index: 1; - top: 34px; - height: 28px; - transform: perspective(2000px) rotateZ(300deg) rotateX(44deg) rotateY(39deg) - scale(1.4); - } - - .vite { - z-index: 0; - top: 107px; - height: 26px; - width: auto; - transform: perspective(2000px) rotateZ(300deg) rotateX(40deg) rotateY(39deg) - scale(0.8); - } -} - -#app { - width: 1126px; - max-width: 100%; - margin: 0 auto; - text-align: center; - border-inline: 1px solid var(--border); - min-height: 100svh; - display: flex; - flex-direction: column; - box-sizing: border-box; -} - -#center { - display: flex; - flex-direction: column; - gap: 25px; - place-content: center; - place-items: center; - flex-grow: 1; - - @media (max-width: 1024px) { - padding: 32px 20px 24px; - gap: 18px; - } -} - -#next-steps { - display: flex; - border-top: 1px solid var(--border); - text-align: left; - - & > div { - flex: 1 1 0; - padding: 32px; - @media (max-width: 1024px) { - padding: 24px 20px; - } - } - - .icon { - margin-bottom: 16px; - width: 22px; - height: 22px; - } - - @media (max-width: 1024px) { - flex-direction: column; - text-align: center; - } -} - -#docs { - border-right: 1px solid var(--border); - - @media (max-width: 1024px) { - border-right: none; - border-bottom: 1px solid var(--border); - } -} - -#next-steps ul { - list-style: none; - padding: 0; - display: flex; - gap: 8px; - margin: 32px 0 0; - - .logo { - height: 18px; - } - - a { - color: var(--text-h); - font-size: 16px; - border-radius: 6px; - background: var(--social-bg); - display: flex; - padding: 6px 12px; - align-items: center; - gap: 8px; - text-decoration: none; - transition: box-shadow 0.3s; - - &:hover { - box-shadow: var(--shadow); - } - .button-icon { - height: 18px; - width: 18px; - } - } - - @media (max-width: 1024px) { - margin-top: 20px; - flex-wrap: wrap; - justify-content: center; - - li { - flex: 1 1 calc(50% - 8px); - } - - a { - width: 100%; - justify-content: center; - box-sizing: border-box; - } - } -} - -#spacer { - height: 88px; - border-top: 1px solid var(--border); - @media (max-width: 1024px) { - height: 48px; - } -} - -.ticks { - position: relative; - width: 100%; - - &::before, - &::after { - content: ''; - position: absolute; - top: -4.5px; - border: 5px solid transparent; - } - - &::before { - left: 0; - border-left-color: var(--border); - } - &::after { - right: 0; - border-right-color: var(--border); - } -} diff --git a/new/frontend/tsconfig.app.json b/new/frontend/tsconfig.app.json deleted file mode 100644 index 5c750c5..0000000 --- a/new/frontend/tsconfig.app.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - "extends": "@vue/tsconfig/tsconfig.dom.json", - "compilerOptions": { - "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo", - "types": ["vite/client"], - - /* Linting */ - "noUnusedLocals": true, - "noUnusedParameters": true, - "erasableSyntaxOnly": true, - "noFallthroughCasesInSwitch": true - }, - "include": ["src/**/*.ts", "src/**/*.tsx", "src/**/*.vue"] -} diff --git a/new/frontend/tsconfig.json b/new/frontend/tsconfig.json deleted file mode 100644 index 1ffef60..0000000 --- a/new/frontend/tsconfig.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "files": [], - "references": [ - { "path": "./tsconfig.app.json" }, - { "path": "./tsconfig.node.json" } - ] -} diff --git a/new/frontend/tsconfig.node.json b/new/frontend/tsconfig.node.json deleted file mode 100644 index d3c52ea..0000000 --- a/new/frontend/tsconfig.node.json +++ /dev/null @@ -1,24 +0,0 @@ -{ - "compilerOptions": { - "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo", - "target": "es2023", - "lib": ["ES2023"], - "module": "esnext", - "types": ["node"], - "skipLibCheck": true, - - /* Bundler mode */ - "moduleResolution": "bundler", - "allowImportingTsExtensions": true, - "verbatimModuleSyntax": true, - "moduleDetection": "force", - "noEmit": true, - - /* Linting */ - "noUnusedLocals": true, - "noUnusedParameters": true, - "erasableSyntaxOnly": true, - "noFallthroughCasesInSwitch": true - }, - "include": ["vite.config.ts"] -} diff --git a/new/frontend/vite.config.ts b/new/frontend/vite.config.ts deleted file mode 100644 index bbcf80c..0000000 --- a/new/frontend/vite.config.ts +++ /dev/null @@ -1,7 +0,0 @@ -import { defineConfig } from 'vite' -import vue from '@vitejs/plugin-vue' - -// https://vite.dev/config/ -export default defineConfig({ - plugins: [vue()], -}) diff --git a/src/gui/crash_dump.txt b/src/gui/crash_dump.txt deleted file mode 100644 index cad70b0..0000000 --- a/src/gui/crash_dump.txt +++ /dev/null @@ -1,103 +0,0 @@ - -============================================================ -[2026-05-12 11:14:51] -Traceback (most recent call last): - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\water_quality_gui.py", line 130, in - from src.gui.panels.step9_panel import Step9Panel - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\panels\step9_panel.py", line 24, in - from src.core.water_quality_inversion_pipeline_GUI import WaterQualityInversionPipeline - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\core\water_quality_inversion_pipeline_GUI.py", line 45, in - from src.preprocessing.process_water_quality_data import process_water_quality_data - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\preprocessing\process_water_quality_data.py", line 9, in - from scipy import stats - File "", line 1412, in _handle_fromlist - File "D:\111\changyongruanjian\anconda\envs\WQ_GUI\Lib\site-packages\scipy\__init__.py", line 143, in __getattr__ - return _importlib.import_module(f'scipy.{name}') - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\111\changyongruanjian\anconda\envs\WQ_GUI\Lib\importlib\__init__.py", line 90, in import_module - return _bootstrap._gcd_import(name[level:], package, level) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "D:\111\changyongruanjian\anconda\envs\WQ_GUI\Lib\site-packages\scipy\stats\__init__.py", line 632, in - from ._multicomp import * - File "D:\111\changyongruanjian\anconda\envs\WQ_GUI\Lib\site-packages\scipy\stats\_multicomp.py", line 11, in - from scipy.stats._qmc import check_random_state - File "D:\111\changyongruanjian\anconda\envs\WQ_GUI\Lib\site-packages\scipy\stats\_qmc.py", line 26, in - from scipy.sparse.csgraph import minimum_spanning_tree - File "D:\111\changyongruanjian\anconda\envs\WQ_GUI\Lib\site-packages\scipy\sparse\csgraph\__init__.py", line 188, in - from ._shortest_path import ( - File "scipy/sparse/csgraph/_shortest_path.pyx", line 21, in init scipy.sparse.csgraph._shortest_path - File "", line 1349, in _find_and_load -KeyboardInterrupt - -============================================================ -[2026-05-12 11:57:28] -Traceback (most recent call last): - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\water_quality_gui.py", line 3123, in - main() - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\water_quality_gui.py", line 3093, in main - _dialog.exec_() -KeyboardInterrupt - -============================================================ -[2026-05-28 15:45:11] -Traceback (most recent call last): - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\water_quality_gui.py", line 3123, in - main() - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\water_quality_gui.py", line 3097, in main - window = WaterQualityGUI() - ^^^^^^^^^^^^^^^^^ - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\water_quality_gui.py", line 1352, in __init__ - self.init_ui() - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\water_quality_gui.py", line 1586, in init_ui - self.create_content_area() - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\water_quality_gui.py", line 1943, in create_content_area - self.step2_panel = Step2Panel() - ^^^^^^^^^^^^ -TypeError: Step2Panel.__init__() missing 1 required positional argument: 'session' - -============================================================ -[2026-05-28 15:45:19] -Traceback (most recent call last): - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\water_quality_gui.py", line 3123, in - main() - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\water_quality_gui.py", line 3097, in main - window = WaterQualityGUI() - ^^^^^^^^^^^^^^^^^ - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\water_quality_gui.py", line 1352, in __init__ - self.init_ui() - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\water_quality_gui.py", line 1586, in init_ui - self.create_content_area() - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\water_quality_gui.py", line 1943, in create_content_area - self.step2_panel = Step2Panel() - ^^^^^^^^^^^^ -TypeError: Step2Panel.__init__() missing 1 required positional argument: 'session' - -============================================================ -[2026-05-28 16:00:53] -Traceback (most recent call last): - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\water_quality_gui.py", line 2149, in on_step_changed - self.auto_populate_step_inputs(item_data) - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\water_quality_gui.py", line 2362, in auto_populate_step_inputs - if step_id not in self.step_dependencies: - ^^^^^^^^^^^^^^^^^^^^^^ -AttributeError: 'WaterQualityGUI' object has no attribute 'step_dependencies'. Did you mean: '_init_step_dependencies'? - -============================================================ -[2026-06-03 13:56:59] -Traceback (most recent call last): - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\water_quality_gui.py", line 3354, in - main() - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\water_quality_gui.py", line 3331, in main - sys.exit(app.exec_()) - ^^^^^^^^^^^ -KeyboardInterrupt - -============================================================ -[2026-06-04 09:54:07] -Traceback (most recent call last): - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\water_quality_gui.py", line 3237, in - main() - File "D:\111\office\ZHLduijie\1.WQ\WQ_GUI\src\gui\water_quality_gui.py", line 3214, in main - sys.exit(app.exec_()) - ^^^^^^^^^^^ -KeyboardInterrupt diff --git a/src/gui/scaler_params.pkl b/src/gui/scaler_params.pkl deleted file mode 100644 index ae05ef2ee1fac25cea47c820b91cc544ea5a3e7f..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1783 zcma)7YgkiP77Y|dQb9x%DiskSQW{b~kOX-wc?biF^0e0C%$h;l)c zi?!lNRqCsb)7G{lA5+?|P{vNBsL%@d7#}zz77*&lR~&&!P-o`n>>p>Jv%hoBT6?ea zD6*-mwBk)0>&E0NDN?5vX?2uVr;$+%qfl=V5pq&ba;!tDo>a?8ojg@WDk&XT%e_&| z<#Rr)-FAhZCR7xu<|^1((*s5?H}`F18m-an9n?fNMd}SYiXjwgf=di!=coyWRB4sw z2t!fk7`s4iP-%@KLZ;DCB9%d@SCBd#Y2?_Mj2Vx#;IT8N=a{`*Z`4wjh;_D@+g-7X zOW}MHUtz7nxTK_{gekL4H;FtwCykypot;ZNxN`G6c7}zi8E{^XMygauMbmo{T7}G< zJjc#Wvd~y%f!?4awc7tpbQWWf%9JF-aI`&ZYpI2WpoJGhlV(1NfhKcbqm!E#u~yOl z;YmBOd{U{@Y$sGIQfn@V+jGq6^#rL@Y*DKywVsd~^%TP;IvS*QmK3I~{Sz(Q;l2fh z&8@EK87eG1{N{1zxhhysx%}LCF&B$(T?)~ghpXkF*7xyfH^^TLj)bvgo?DZtrN4wQjcp@0)}RzeMrClSBk__a`D<`8^J+ z2Ome&`98laJl19#sUtYr?fEUB-DwYciRA>)OKC6Kn|7iF74$NTn6qDMY4IPY?EJZe ztOP@uh7ZWLVdIzU1dN-xezxDxQTr9xD$sDf%{d4hTQyO7UWNR|mI3dmt*A|iP4S6j z5OpQya!s%SxnpM*mh8<#kh5n`OTi8t?Z5E%xho4$;baPJ*i!`0#h0ZW} zcj0$mwbW;H6yapT@Rf3p5`YogIArhaCFc)#`;(~64wqj1q)Mf=tE(PaDFmg zsK~1mzSML2ouU3OMfv|#l-KZCZPN$g__j&)ee_Tu)X(sb__u|ax>59$UoXVQ1H)sD zk$$*N)+Dd)7NKXuK<&&U{y1l}zD%(Jh)w(M#O>Tb94uVX-f?v~{>0`*D93|9x@G+< zXfhbfBH3G4twK;hpW&|&i1GFv!qLWGf>{A!ZBP3o7?4#R;1`DC-uTq_K9!+RpKctR z%oju3={7bX4Z-oj`M)N`g+Op&WSv852!w{bt#dL%aMvah_Pc5L@=Z8Z=v}v=wS0ebWGH1Sj2*%|N%t!OZh+W?6nvxj`s|>@s z%-=%M(-5%j)ZuWfzH0SuodS42ow=uJ2b$)s>2FU6!