diff --git a/_smoke_new_arch.py b/_smoke_new_arch.py index 112ca8f..04f5bc1 100644 --- a/_smoke_new_arch.py +++ b/_smoke_new_arch.py @@ -314,6 +314,82 @@ def smoke_e2e(): "[Service✗]" in log_text and "execute_step2" in log_text, f"log 片段：{log_text[-200:]!r}") + # ---- 切到 step6（真实 view + 真实 service） ---- + win.nav_list.setCurrentRow(5) + view_step6 = win._views.get("step6") + report("L3", "_views['step6'] 是真实 Step6View（已迁移）", + type(view_step6).__name__ == "Step6View", + f"type={type(view_step6).__name__}") + + win.log_text.clear() + view_step6.run_btn.click() + loop = QEventLoop() + QTimer.singleShot(1500, loop.quit) + loop.exec_() + + log_text = win.log_text.toPlainText() + report("L3", "step6 真实 service 已迁移：空 deglint_img_path 触发 Service✗ 错误分支", + "[Service✗]" in log_text and "execute_step6" in log_text, + f"log 片段：{log_text[-200:]!r}") + + # ---- 切到 step7（真实 view + 真实 service） ---- + win.nav_list.setCurrentRow(6) + view_step7 = win._views.get("step7") + report("L3", "_views['step7'] 是真实 Step7View（已迁移）", + type(view_step7).__name__ == "Step7View", + f"type={type(view_step7).__name__}") + + win.log_text.clear() + view_step7.run_btn.click() + loop = QEventLoop() + QTimer.singleShot(1500, loop.quit) + loop.exec_() + + log_text = win.log_text.toPlainText() + report("L3", "step7 真实 service 已迁移：空 training_csv_path 触发 Service✗ 错误分支", + "[Service✗]" in log_text and "execute_step7" in log_text, + f"log 片段：{log_text[-200:]!r}") + + # ---- 切到 step8（真实 view + 真实 service） ---- + win.nav_list.setCurrentRow(7) + view_step8 = win._views.get("step8") + report("L3", "_views['step8'] 是真实 Step8View（已迁移）", + type(view_step8).__name__ == "Step8View", + f"type={type(view_step8).__name__}") + + # step8 默认 enable_checkbox=False → service 走 skipped；强制开启以触发 Service✗ + if hasattr(view_step8, "enable_checkbox"): + view_step8.enable_checkbox.setChecked(True) + + win.log_text.clear() + view_step8.run_btn.click() + loop = QEventLoop() + QTimer.singleShot(1500, loop.quit) + loop.exec_() + + log_text = win.log_text.toPlainText() + report("L3", "step8 真实 service 已迁移：空 training_csv_path 触发 Service✗ 错误分支", + "[Service✗]" in log_text and "execute_step8" in log_text, + f"log 片段：{log_text[-200:]!r}") + + # ---- 切到 step9（真实 view + 真实 service） ---- + win.nav_list.setCurrentRow(8) + view_step9 = win._views.get("step9") + report("L3", "_views['step9'] 是真实 Step9View（已迁移）", + type(view_step9).__name__ == "Step9View", + f"type={type(view_step9).__name__}") + + win.log_text.clear() + view_step9.run_btn.click() + loop = QEventLoop() + QTimer.singleShot(1500, loop.quit) + loop.exec_() + + log_text = win.log_text.toPlainText() + report("L3", "step9 真实 service 已迁移：空 sampling_csv_path 触发 Service✗ 错误分支", + "[Service✗]" in log_text and "execute_step9" in log_text, + f"log 片段：{log_text[-200:]!r}") + win.close() diff --git a/src/new/main_view.py b/src/new/main_view.py index 7f24119..7c15880 100644 --- a/src/new/main_view.py +++ b/src/new/main_view.py @@ -106,32 +106,32 @@ ROUTES = [ "name": "6. 光谱特征", "view_module": "src.new.views.step6_view", "view_class": "Step6View", - "service_module": "src.new.services.placeholder_service", - "service_func": "execute_placeholder", + "service_module": "src.new.services.step6_service", + "service_func": "execute_step6", }, { "id": "step7", "name": "7. 水质光谱指数", "view_module": "src.new.views.step7_view", "view_class": "Step7View", - "service_module": "src.new.services.placeholder_service", - "service_func": "execute_placeholder", + "service_module": "src.new.services.step7_service", + "service_func": "execute_step7", }, { "id": "step8", "name": "8. 机器学习建模", "view_module": "src.new.views.step8_view", "view_class": "Step8View", - "service_module": "src.new.services.placeholder_service", - "service_func": "execute_placeholder", + "service_module": "src.new.services.step8_service", + "service_func": "execute_step8", }, { "id": "step9", "name": "9. 机器学习预测", "view_module": "src.new.views.step9_view", "view_class": "Step9View", - "service_module": "src.new.services.placeholder_service", - "service_func": "execute_placeholder", + "service_module": "src.new.services.step9_service", + "service_func": "execute_step9", }, { "id": "step10", @@ -233,7 +233,7 @@ class MainView(QMainWindow): self._build_routes() self._log("[Boot] MainView 初始化完成") - self._log(f"[Boot] 已注册 {len(ROUTES)} 条路由（13 个 view 全部真实，前 5 个步骤 service 已迁移）") + self._log(f"[Boot] 已注册 {len(ROUTES)} 条路由（13 个 view 全部真实，前 9 个步骤 service 已迁移）") # ------------------------------------------------------------------ # UI 布局：左侧导航 + 右侧 stacked + 底部日志 diff --git a/src/new/services/step6_service.py b/src/new/services/step6_service.py new file mode 100644 index 0000000..12e6064 --- /dev/null +++ b/src/new/services/step6_service.py @@ -0,0 +1,171 @@ +# -*- coding: utf-8 -*- +""" +Step6 后端计算服务（光谱特征提取） +==================================== + +纯计算函数——绝对不引用 PyQt、绝对不引用 main_view、绝对不读写全局变量。它只： + +1. 从 ``config`` 字典读取参数； +2. 调用旧版 ``DataPreparationStep.extract_training_spectra`` 在去耀斑影像 + 上按采样点坐标提取平均光谱； +3. 返回结果字典 ``{status, output_path, message, mode}``。 + +调用入口（由 main_view 在后台 QThread 中调用）： + + execute_step6({ + "deglint_img_path": "D:/deglint.bsq", # 去耀斑影像路径 + "csv_path": "D:/processed_data.csv", # 采样点坐标 CSV + "boundary_path": "D:/water_mask.dat", # 水域掩膜（可选，自动回退到 water_mask_path） + "glint_mask_path": "D:/severe_glint.dat", # 耀斑掩膜（独立运行时必填） + "water_mask_path": "D:/water_mask.dat", # 别名（与 boundary_path 二选一） + "radius": 5, # 采样半径 + "source_epsg": 4326, # 源坐标系 + "enabled": True, + "output_path": "D:/training_spectra.csv", # 可选，未指定则用 work_dir 约定目录 + "work_dir": "D:/workspace", # 工作目录（main_view 注入） + }) + +返回字典字段： + +* ``status`` : "completed" | "skipped" | "error" +* ``output_path`` : 生成的 training_spectra.csv 路径（失败时为 None） +* ``message`` : 人类可读说明 +* ``mode`` : "extract_spectra"（便于 UI 提示） +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any, Dict + +from src.core.steps.data_preparation_step import DataPreparationStep + + +def _resolve_output_dir(output_path: str | None, work_dir: str) -> Path: + """根据 output_path / work_dir 计算 training_spectra.csv 输出目录""" + if output_path: + return Path(output_path).parent + return Path(work_dir) / "6_Spectral_Feature_Extraction" + + +def execute_step6(config: Dict[str, Any]) -> Dict[str, Any]: + """Step 6 后端计算入口——纯函数 + + Args: + config: 由前端 view.get_config() 序列化、再经 main_view 注入 work_dir 的字典 + + Returns: + 标准结果字典 ``{status, output_path, message, mode}`` + """ + # ---------- 入参规整 ---------- + deglint_img_path = config.get("deglint_img_path") + csv_path = config.get("csv_path") + boundary_path = config.get("boundary_path") + glint_mask_path = config.get("glint_mask_path") + water_mask_path = config.get("water_mask_path") + radius = int(config.get("radius", 5)) + source_epsg = int(config.get("source_epsg", 4326)) + enabled = bool(config.get("enabled", True)) + output_path = config.get("output_path") + work_dir = config.get("work_dir") or "." + + output_dir = _resolve_output_dir(output_path, work_dir) + mode = "extract_spectra" + + # ---------- 提前失败检查 ---------- + if not enabled: + return { + "status": "skipped", + "output_path": None, + "message": "用户禁用此步骤（enabled=False）", + "mode": mode, + } + if not deglint_img_path: + return { + "status": "error", + "output_path": None, + "message": "未提供去耀斑影像路径（deglint_img_path）", + "mode": mode, + } + if not csv_path: + return { + "status": "error", + "output_path": None, + "message": "未提供采样点 CSV 路径（csv_path）", + "mode": mode, + } + if not Path(deglint_img_path).exists(): + return { + "status": "error", + "output_path": None, + "message": f"去耀斑影像不存在: {deglint_img_path}", + "mode": mode, + } + if not Path(csv_path).exists(): + return { + "status": "error", + "output_path": None, + "message": f"采样点 CSV 不存在: {csv_path}", + "mode": mode, + } + + # ---------- 显式构造 output_path（如未指定）---------- + if not output_path: + output_path = str(output_dir / "training_spectra.csv").replace("\\", "/") + + # ---------- 智能回退：boundary_path ← water_mask_path ---------- + final_boundary_path = boundary_path or water_mask_path + + # ---------- 执行（包一层 try/except 把异常转 dict，避免炸线程） ---------- + try: + print(f"[Step6 Service] 提取光谱: deglint={deglint_img_path}, csv={csv_path}") + print(f"[Step6 Service] 采样半径={radius}, EPSG={source_epsg}") + result_path = DataPreparationStep.extract_training_spectra( + deglint_img_path=deglint_img_path, + radius=radius, + source_epsg=source_epsg, + csv_path=csv_path, + boundary_path=final_boundary_path, + glint_mask_path=glint_mask_path, + water_mask_path=water_mask_path, + output_dir=output_dir, + callback=None, # 日志由 main_view 统一接管 + ) + except FileNotFoundError as e: + return { + "status": "error", + "output_path": None, + "message": f"文件不存在: {e}", + "mode": mode, + } + except ValueError as e: + return { + "status": "error", + "output_path": None, + "message": f"参数错误: {e}", + "mode": mode, + } + except Exception as e: # noqa: BLE001 —— service 层兜底捕获所有 + return { + "status": "error", + "output_path": None, + "message": f"{type(e).__name__}: {e}", + "mode": mode, + } + + # ---------- 成功路径 ---------- + p = Path(result_path) + if not p.exists(): + return { + "status": "error", + "output_path": None, + "message": f"DataPreparationStep.extract_training_spectra 未生成文件: {result_path}", + "mode": mode, + } + + return { + "status": "completed", + "output_path": str(p).replace("\\", "/"), + "message": f"训练光谱已保存: {p.name}", + "mode": mode, + } \ No newline at end of file diff --git a/src/new/services/step7_service.py b/src/new/services/step7_service.py new file mode 100644 index 0000000..8068061 --- /dev/null +++ b/src/new/services/step7_service.py @@ -0,0 +1,166 @@ +# -*- coding: utf-8 -*- +""" +Step7 后端计算服务（水质光谱指数计算） +==================================== + +纯计算函数——绝对不引用 PyQt、绝对不引用 main_view、绝对不读写全局变量。它只： + +1. 从 ``config`` 字典读取参数； +2. 调用旧版 ``DataPreparationStep.calculate_water_quality_indices`` 在训练光谱 + CSV 上批量计算指定公式（band_math 方法）； +3. 返回结果字典 ``{status, output_path, message, mode}``。 + +调用入口（由 main_view 在后台 QThread 中调用）： + + execute_step7({ + "training_csv_path": "D:/training_spectra.csv", # 输入光谱 CSV + "formula_csv_file": ".../src/gui/model/waterindex.csv", # 公式 CSV（必填） + "formula_names": ["NDCI", "Chl_Conc_NDCI", ...], # 指定公式；None=全量 + "enabled": True, + "output_file": "D:/training_spectra_indices.csv", # 可选 + "work_dir": "D:/workspace", # 工作目录（main_view 注入） + }) + +返回字典字段： + +* ``status`` : "completed" | "skipped" | "error" +* ``output_path`` : 生成的 indices CSV 路径（失败时为 None） +* ``message`` : 人类可读说明 +* ``mode`` : "calc_indices"（便于 UI 提示） +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any, Dict, List, Optional + +from src.core.steps.data_preparation_step import DataPreparationStep + + +def _resolve_output_dir(output_file: str | None, work_dir: str) -> Path: + """根据 output_file / work_dir 计算 indices CSV 输出目录""" + if output_file: + return Path(output_file).parent + return Path(work_dir) / "7_Water_Quality_Indices" + + +def execute_step7(config: Dict[str, Any]) -> Dict[str, Any]: + """Step 7 后端计算入口——纯函数 + + Args: + config: 由前端 view.get_config() 序列化、再经 main_view 注入 work_dir 的字典 + + Returns: + 标准结果字典 ``{status, output_path, message, mode}`` + """ + # ---------- 入参规整 ---------- + training_csv_path: Optional[str] = config.get("training_csv_path") + formula_csv_file: Optional[str] = config.get("formula_csv_file") + formula_names: Optional[List[str]] = config.get("formula_names") + enabled: bool = bool(config.get("enabled", True)) + output_file: Optional[str] = config.get("output_file") + work_dir: str = config.get("work_dir") or "." + + output_dir = _resolve_output_dir(output_file, work_dir) + mode = "calc_indices" + + # ---------- 提前失败检查 ---------- + if not enabled: + return { + "status": "skipped", + "output_path": None, + "message": "用户禁用此步骤（enabled=False）", + "mode": mode, + } + if not training_csv_path: + return { + "status": "error", + "output_path": None, + "message": "未提供训练光谱 CSV 路径（training_csv_path）", + "mode": mode, + } + if not formula_csv_file: + return { + "status": "error", + "output_path": None, + "message": "未提供公式 CSV 路径（formula_csv_file）", + "mode": mode, + } + if not Path(training_csv_path).exists(): + return { + "status": "error", + "output_path": None, + "message": f"训练光谱 CSV 不存在: {training_csv_path}", + "mode": mode, + } + if not Path(formula_csv_file).exists(): + return { + "status": "error", + "output_path": None, + "message": f"公式 CSV 不存在: {formula_csv_file}", + "mode": mode, + } + + # ---------- 显式构造 output_file（如未指定）---------- + if not output_file: + output_file = str(output_dir / "training_spectra_indices.csv").replace("\\", "/") + + # ---------- 执行（包一层 try/except 把异常转 dict，避免炸线程） ---------- + try: + formula_count = len(formula_names) if formula_names else "全量" + print(f"[Step7 Service] 计算水质指数: csv={training_csv_path}, formula_count={formula_count}") + result_path = DataPreparationStep.calculate_water_quality_indices( + training_csv_path=training_csv_path, + formula_csv_file=formula_csv_file, + formula_names=formula_names, + output_file=output_file, + enabled=enabled, + output_dir=output_dir, + callback=None, # 日志由 main_view 统一接管 + ) + except FileNotFoundError as e: + return { + "status": "error", + "output_path": None, + "message": f"文件不存在: {e}", + "mode": mode, + } + except ValueError as e: + return { + "status": "error", + "output_path": None, + "message": f"参数错误: {e}", + "mode": mode, + } + except Exception as e: # noqa: BLE001 —— service 层兜底捕获所有 + return { + "status": "error", + "output_path": None, + "message": f"{type(e).__name__}: {e}", + "mode": mode, + } + + # ---------- 成功路径 ---------- + if not result_path: + return { + "status": "skipped", + "output_path": None, + "message": "水质指数计算被跳过（enabled=False 或无产物）", + "mode": mode, + } + + p = Path(result_path) + if not p.exists(): + return { + "status": "error", + "output_path": None, + "message": f"DataPreparationStep.calculate_water_quality_indices 未生成文件: {result_path}", + "mode": mode, + } + + return { + "status": "completed", + "output_path": str(p).replace("\\", "/"), + "message": f"水质指数已保存: {p.name}", + "mode": mode, + } \ No newline at end of file diff --git a/src/new/services/step8_service.py b/src/new/services/step8_service.py new file mode 100644 index 0000000..05350a9 --- /dev/null +++ b/src/new/services/step8_service.py @@ -0,0 +1,147 @@ +# -*- coding: utf-8 -*- +""" +Step8 后端计算服务（机器学习建模训练） +==================================== + +纯计算函数——绝对不引用 PyQt、绝对不引用 main_view、绝对不读写全局变量。它只： + +1. 从 ``config`` 字典读取参数； +2. 调用旧版 ``ModelingStep.train_models`` 在训练光谱 CSV 上做 + GridSearchCV 多模型/多预处理/多划分方法的批量训练； +3. 返回结果字典 ``{status, output_path, message, mode}``。 + +调用入口（由 main_view 在后台 QThread 中调用）： + + execute_step8({ + "training_csv_path": "D:/training_spectra_indices.csv", # 训练 CSV（必填） + "feature_start_column": "374.285004", # 特征起始列名/索引 + "preprocessing_methods": ["None", "MMS"], # 预处理方法列表 + "model_names": ["RF", "SVR", "Ridge", "Lasso"], # 模型列表 + "split_methods": ["spxy"], # 划分方法列表 + "cv_folds": 5, # 交叉验证折数 + "enabled": True, + "output_path": "D:/8_Supervised_Model_Training", # 可选，模型保存目录 + "work_dir": "D:/workspace", # 工作目录（main_view 注入） + }) + +返回字典字段： + +* ``status`` : "completed" | "skipped" | "error" +* ``output_path`` : 模型保存目录路径（失败时为 None） +* ``message`` : 人类可读说明 +* ``mode`` : "train_ml"（便于 UI 提示） +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any, Dict, List, Optional + +from src.core.steps.modeling_step import ModelingStep + + +def _resolve_output_dir(output_path: str | None, work_dir: str) -> Path: + """根据 output_path / work_dir 计算模型保存目录""" + if output_path: + return Path(output_path) + return Path(work_dir) / "8_Supervised_Model_Training" + + +def execute_step8(config: Dict[str, Any]) -> Dict[str, Any]: + """Step 8 后端计算入口——纯函数 + + Args: + config: 由前端 view.get_config() 序列化、再经 main_view 注入 work_dir 的字典 + + Returns: + 标准结果字典 ``{status, output_path, message, mode}`` + """ + # ---------- 入参规整 ---------- + training_csv_path: Optional[str] = config.get("training_csv_path") + feature_start_column: str = str(config.get("feature_start_column", "374.285004")) + preprocessing_methods: Optional[List[str]] = config.get("preprocessing_methods") + model_names: Optional[List[str]] = config.get("model_names") + split_methods: Optional[List[str]] = config.get("split_methods") + cv_folds: int = int(config.get("cv_folds", 5)) + enabled: bool = bool(config.get("enabled", True)) + output_path: Optional[str] = config.get("output_path") + work_dir: str = config.get("work_dir") or "." + + output_dir = _resolve_output_dir(output_path, work_dir) + mode = "train_ml" + + # ---------- 提前失败检查 ---------- + if not enabled: + return { + "status": "skipped", + "output_path": None, + "message": "用户禁用此步骤（enabled=False）", + "mode": mode, + } + if not training_csv_path: + return { + "status": "error", + "output_path": None, + "message": "未提供训练 CSV 路径（training_csv_path）", + "mode": mode, + } + if not Path(training_csv_path).exists(): + return { + "status": "error", + "output_path": None, + "message": f"训练 CSV 不存在: {training_csv_path}", + "mode": mode, + } + + # ---------- 兜底默认值（与旧 panel 默认对齐）---------- + if not preprocessing_methods: + preprocessing_methods = ["None", "MMS"] + if not model_names: + model_names = ["SVR"] + if not split_methods: + split_methods = ["spxy"] + + # ---------- 执行（包一层 try/except 把异常转 dict，避免炸线程） ---------- + try: + print(f"[Step8 Service] 训练 ML 模型: csv={training_csv_path}") + print(f"[Step8 Service] 模型={model_names}, 预处理={preprocessing_methods}, 划分={split_methods}, CV={cv_folds}") + result_dir = ModelingStep.train_models( + feature_start_column=feature_start_column, + preprocessing_methods=preprocessing_methods, + model_names=model_names, + split_methods=split_methods, + cv_folds=cv_folds, + training_csv_path=training_csv_path, + output_dir=output_dir, + callback=None, # 日志由 main_view 统一接管 + ) + except FileNotFoundError as e: + return { + "status": "error", + "output_path": None, + "message": f"文件不存在: {e}", + "mode": mode, + } + except ValueError as e: + return { + "status": "error", + "output_path": None, + "message": f"参数错误: {e}", + "mode": mode, + } + except Exception as e: # noqa: BLE001 —— service 层兜底捕获所有 + return { + "status": "error", + "output_path": None, + "message": f"{type(e).__name__}: {e}", + "mode": mode, + } + + # ---------- 成功路径 ---------- + p = Path(result_dir) + return { + "status": "completed", + "output_path": str(p).replace("\\", "/"), + "message": f"模型训练完成，结果保存在: {p.name or p}", + "mode": mode, + } \ No newline at end of file diff --git a/src/new/services/step9_service.py b/src/new/services/step9_service.py new file mode 100644 index 0000000..669fc5c --- /dev/null +++ b/src/new/services/step9_service.py @@ -0,0 +1,154 @@ +# -*- coding: utf-8 -*- +""" +Step9 后端计算服务（机器学习预测校正） +==================================== + +纯计算函数——绝对不引用 PyQt、绝对不引用 main_view、绝对不读写全局变量。它只： + +1. 从 ``config`` 字典读取参数； +2. 调用旧版 ``PredictionStep.predict_water_quality`` 用训练好的 ML 模型 + 对采样点 CSV 做批量预测，输出每个目标参数的预测 CSV； +3. 返回结果字典 ``{status, output_path, message, mode}``。 + +调用入口（由 main_view 在后台 QThread 中调用）： + + execute_step9({ + "sampling_csv_path": "D:/sampling_spectra.csv", # 采样点光谱 CSV（必填） + "models_dir": "D:/8_Supervised_Model_Training", # 模型目录（必填） + "metric": "test_r2", # 模型选择指标 + "prediction_column": "prediction", # 预测列名 + "enabled": True, + "output_path": "D:/9_ML_Prediction", # 可选，预测输出目录 + "work_dir": "D:/workspace", # 工作目录（main_view 注入） + }) + +返回字典字段： + +* ``status`` : "completed" | "skipped" | "error" +* ``output_path`` : 预测输出目录路径（失败时为 None） +* ``message`` : 人类可读说明 +* ``mode`` : "predict_ml"（便于 UI 提示） + +注意：外部导入模型（``_external_models_dict``）模式暂未支持——该模式需要 +在主线程维护一个 dict of model objects，无法 pickle 跨 QThread 传递。 +如需启用，需在 main_view 端新增"先扫盘加载 → 转 joblib 路径 → 传 dict" +的预处理链路。 +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any, Dict + +from src.core.steps.prediction_step import PredictionStep + + +def _resolve_output_dir(output_path: str | None, work_dir: str) -> Path: + """根据 output_path / work_dir 计算预测结果输出目录""" + if output_path: + return Path(output_path) + # 与旧 pipeline 一致：prediction_dir / 9_ML_Prediction + return Path(work_dir) / "prediction" / "9_ML_Prediction" + + +def execute_step9(config: Dict[str, Any]) -> Dict[str, Any]: + """Step 9 后端计算入口——纯函数 + + Args: + config: 由前端 view.get_config() 序列化、再经 main_view 注入 work_dir 的字典 + + Returns: + 标准结果字典 ``{status, output_path, message, mode}`` + """ + # ---------- 入参规整 ---------- + sampling_csv_path: str = config.get("sampling_csv_path") or "" + models_dir: str = config.get("models_dir") or "" + metric: str = str(config.get("metric", "test_r2")) + prediction_column: str = str(config.get("prediction_column", "prediction")) + enabled: bool = bool(config.get("enabled", True)) + output_path: str = config.get("output_path") + work_dir: str = config.get("work_dir") or "." + + output_dir = _resolve_output_dir(output_path, work_dir) + mode = "predict_ml" + + # ---------- 提前失败检查 ---------- + if not enabled: + return { + "status": "skipped", + "output_path": None, + "message": "用户禁用此步骤（enabled=False）", + "mode": mode, + } + if not sampling_csv_path: + return { + "status": "error", + "output_path": None, + "message": "未提供采样点 CSV 路径（sampling_csv_path）", + "mode": mode, + } + if not models_dir: + return { + "status": "error", + "output_path": None, + "message": "未提供模型目录（models_dir）", + "mode": mode, + } + if not Path(sampling_csv_path).exists(): + return { + "status": "error", + "output_path": None, + "message": f"采样点 CSV 不存在: {sampling_csv_path}", + "mode": mode, + } + if not Path(models_dir).exists(): + return { + "status": "error", + "output_path": None, + "message": f"模型目录不存在: {models_dir}", + "mode": mode, + } + + # ---------- 执行（包一层 try/except 把异常转 dict，避免炸线程） ---------- + try: + print(f"[Step9 Service] 预测水质参数: sampling={sampling_csv_path}") + print(f"[Step9 Service] models_dir={models_dir}, metric={metric}") + prediction_files = PredictionStep.predict_water_quality( + sampling_csv_path=sampling_csv_path, + models_dir=models_dir, + metric=metric, + prediction_column=prediction_column, + output_dir=output_dir, + callback=None, # 日志由 main_view 统一接管 + ) + except FileNotFoundError as e: + return { + "status": "error", + "output_path": None, + "message": f"文件不存在: {e}", + "mode": mode, + } + except ValueError as e: + return { + "status": "error", + "output_path": None, + "message": f"参数错误: {e}", + "mode": mode, + } + except Exception as e: # noqa: BLE001 —— service 层兜底捕获所有 + return { + "status": "error", + "output_path": None, + "message": f"{type(e).__name__}: {e}", + "mode": mode, + } + + # ---------- 成功路径 ---------- + p = Path(output_dir) + pred_count = len(prediction_files) if isinstance(prediction_files, dict) else 0 + return { + "status": "completed", + "output_path": str(p).replace("\\", "/"), + "message": f"预测完成，共生成 {pred_count} 个目标参数的预测 CSV（目录: {p.name or p}）", + "mode": mode, + } \ No newline at end of file