全链路路径对齐：注册表重写为字符串格式，10_sampling→4_sampling，water_quality_indices→training_spectra_indices

src/core/non_empirical_retrieval.py

@@ -247,7 +247,7 @@ def non_empirical_retrieval(algorithm, model_info_path, coor_spectral_path, outp
 if __name__ == "__main__":
     algorithm= "chl_a"
     model_info_path= r"E:\code\WQ\pipeline_result\work_dir\5_training_spectra\8_non_empirical_models\SS\SS_chl_a.json"
-    coor_spectral_path= r"E:\code\WQ\pipeline_result\work_dir\10_sampling\sampling_spectra.csv"
+    coor_spectral_path= r"E:\code\WQ\pipeline_result\work_dir\4_sampling\sampling_spectra.csv"
     output_path= r"E:\code\WQ\pipeline_result\work_dir\11_12_13_predictions\SS_chl_a.csv"
     wave_radius=5.0
     non_empirical_retrieval(algorithm, model_info_path, coor_spectral_path, output_path, wave_radius)

src/core/steps/prediction_step.py

@@ -24,7 +24,7 @@ class PredictionStep:
         chunk_size: int = 1000,
         water_mask_path: Optional[str] = None,
         glint_mask_path: Optional[str] = None,
-        output_dir: Union[str, Path] = "./10_sampling",
+        output_dir: Union[str, Path] = "./4_sampling",
         callback: Optional[Callable] = None,
         use_adaptive_sampling: bool = True,
     ) -> str:

src/core/water_quality_inversion_pipeline_GUI.py

@@ -144,7 +144,7 @@ class WaterQualityInversionPipeline:
         self.models_dir = self.work_dir / "7_Supervised_Model_Training"
         self.non_empirical_models_dir = self.work_dir / "8_Regression_Modeling"
         self.custom_regression_dir = self.work_dir / "9_Custom_Regression_Modeling"
-        self.sampling_dir = self.work_dir / "10_sampling"
+        self.sampling_dir = self.work_dir / "4_sampling"
         self.prediction_dir = self.work_dir / "11_12_13_predictions"
         self.visualization_dir = self.work_dir / "14_visualization"
         self.reports_dir = self.work_dir / "reports"
@@ -2276,7 +2276,7 @@ def main():
         'step6': {
             'formula_csv_file': 'path/to/water_quality_formulas.csv',  # 公式CSV文件路径
             'formula_names': ['Al10SABI', 'TurbBe16RedOverViolet'],  # 要计算的公式名称列表
-            'output_filename': 'water_quality_indices.csv',
+            'output_filename': 'training_spectra_indices.csv',
             'enabled': True  # 是否启用水质指数计算
         },
         'step7': {

src/gui/water_quality_gui.py

@@ -1365,43 +1365,17 @@ class WaterQualityGUI(QMainWindow):
         """初始化步骤依赖关系和标准输出路径"""
         # 定义每个步骤的标准输出路径模式（相对于工作目录）
         self.step_default_outputs = {
-            'step1': {
+            'step1': "1_water_mask/water_mask_from_ndwi.dat",
-                'water_mask_ndwi': '1_water_mask/water_mask_from_ndwi.dat',
+            'step2': "2_glint/severe_glint_area.dat",
-                'water_mask_shp': '1_water_mask/water_mask_from_shp.dat',
+            'step3': "3_deglint/deglint_kutser.bsq",
-                'hsi_preview': '1_water_mask/hsi_preview.png',
+            'step4_sampling': "4_sampling/sampling_spectra.csv",
-                'water_mask_overlay': '1_water_mask/water_mask_overlay.png'
+            'step5_clean': "4_processed_data/processed_data.csv",
-            },
+            'step6_feature': "5_training_spectra/training_spectra.csv",
-            'step2': {
+            'step7_index': "6_water_quality_indices/training_spectra_indices.csv",
-                'glint_mask': '2_glint/severe_glint_area.dat'
+            'step8_ml_train': "7_Supervised_Model_Training/",
-            },
+            'step9_ml_predict': "11_12_13_predictions/Machine_Learning_Prediction/",
-            'step3': {
+            'step10_watercolor': "8_WaterIndex_Images/",
-                'deglint_kutser': '3_deglint/deglint_kutser.bsq',
+            'step11_map': "14_visualization/"
-                'deglint_goodman': '3_deglint/deglint_goodman.bsq',
-                'deglint_hedley': '3_deglint/deglint_hedley.bsq',
-                'deglint_sugar': '3_deglint/deglint_sugar.bsq',
-                'deglint_interpolated': '3_deglint/interpolated_*.bsq'
-            },
-            'step5_clean': {
-                'processed_data': '4_processed_data/processed_data.csv'
-            },
-            'step6_feature': {
-                'training_spectra': '5_training_spectra/training_spectra.csv'
-            },
-            'step7_index': {
-                'water_indices': '6_water_quality_indices/water_quality_indices.csv'
-            },
-            'step8_ml_train': {
-                'models': '7_Supervised_Model_Training/'
-            },
-            'step4_sampling': {
-                'sampling_points': '10_sampling/sampling_spectra.csv'
-            },
-            'step9_ml_predict': {
-                'predictions': '11_12_13_predictions/Machine_Learning_Prediction/'
-            },
-            'step11_map': {
-                'distribution_maps': '14_visualization/'
-            }
         }
         
         # 定义步骤间的依赖关系：{当前步骤: {输入字段: (依赖步骤, 输出类型, 面板属性名)}}

src/utils/sampling.py

@@ -1049,7 +1049,7 @@ if __name__ == "__main__":
     bil_file = r"E:\wq_gui_test\3_deglint\deglint_goodman.bsq"
     water_mask_shp = r"E:\wq_gui_test\1_water_mask\water_mask_from_shp.dat"
     severe_glint = r"E:\wq_gui_test\2_glint\severe_glint_area.dat"
-    output_csvpath = r"E:\wq_gui_test\10_sampling\sampling_spectra.csv"
+    output_csvpath = r"E:\wq_gui_test\4_sampling\sampling_spectra.csv"
 
     # 设置参数
     interval = 50  # 基础采样点间隔（像元数），当use_adaptive_sampling=False时使用