refactor: 建立动态面板注册表，消除硬编码，实现步骤界面的数据驱动渲染与依赖路由
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src/gui/core/panel_registry.py
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253
src/gui/core/panel_registry.py
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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面板注册中心
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集中定义所有步骤面板的结构化配置，包括：
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- 步骤 ID / 类引用 / 标题 / 图标 / 阶段 / 导航显示名
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- 步骤间依赖关系（输入字段 → 上游步骤/输出类型/面板属性）
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- 构造参数（如 Step13ReportPanel 需要 main_window）
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WaterQualityGUI 通过遍历 PANEL_REGISTRY 动态生成导航树、Tab 页、
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依赖传递和配置读写，彻底消除硬编码。
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"""
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from src.gui.panels.step1_panel import Step1Panel
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from src.gui.panels.step2_panel import Step2Panel
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from src.gui.panels.step3_panel import Step3Panel
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from src.gui.panels.step4_sampling_panel import Step4SamplingPanel
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from src.gui.panels.step5_clean_panel import Step5CleanPanel
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from src.gui.panels.step6_feature_panel import Step6FeaturePanel
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from src.gui.panels.step7_index_panel import Step7IndexPanel
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from src.gui.panels.step8_ml_train_panel import Step8MlTrainPanel
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from src.gui.panels.step9_ml_predict_panel import Step9MlPredictPanel
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from src.gui.panels.step10_watercolor_panel import Step10WatercolorPanel
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from src.gui.panels.step11_map_panel import Step11MapPanel
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from src.gui.panels.step12_viz_panel import Step12VizPanel
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from src.gui.panels.step13_report_panel import Step13ReportPanel
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PANEL_REGISTRY = [
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# ═══════════════════════════════════════════════════════════════
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# 阶段一：影像预处理
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# ═══════════════════════════════════════════════════════════════
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{
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'step_id': 'step1',
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'class_ref': Step1Panel,
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'title': '水域掩膜',
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'icon': '1.png',
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'stage': '阶段一：影像预处理',
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'display_name': '1. 水域掩膜生成',
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'dependencies': None,
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'constructor_kwargs': None,
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},
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{
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'step_id': 'step2',
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'class_ref': Step2Panel,
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'title': '耀斑检测',
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'icon': '2.png',
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'stage': '阶段一：影像预处理',
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'display_name': '2. 耀斑区域识别',
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'dependencies': {
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'img_path': ('step1', 'reference_img', 'img_file'),
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'water_mask_path': ('step1', 'water_mask', 'water_mask_file'),
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},
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'constructor_kwargs': None,
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},
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{
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'step_id': 'step3',
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'class_ref': Step3Panel,
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'title': '耀斑去除',
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'icon': '3.png',
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'stage': '阶段一：影像预处理',
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'display_name': '3. 耀斑去除与修复',
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'dependencies': {
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'img_path': ('step1', 'reference_img', 'img_file'),
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'water_mask': ('step1', 'water_mask', 'water_mask_file'),
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},
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'constructor_kwargs': None,
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},
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# ═══════════════════════════════════════════════════════════════
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# 阶段二：样本数据准备
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# ═══════════════════════════════════════════════════════════════
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{
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'step_id': 'step4_sampling',
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'class_ref': Step4SamplingPanel,
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'title': '采样点布设',
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'icon': '4.png',
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'stage': '阶段二：样本数据准备',
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'display_name': '4. 采样点布设',
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'dependencies': {
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'deglint_img_path': ('step3', 'deglint_image', 'deglint_img_file'),
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'water_mask_path': ('step1', 'water_mask', 'water_mask_file'),
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},
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'constructor_kwargs': None,
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},
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{
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'step_id': 'step5_clean',
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'class_ref': Step5CleanPanel,
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'title': '数据清洗',
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'icon': '5.png',
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'stage': '阶段二：样本数据准备',
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'display_name': '5. 数据清洗',
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# 业务要求保持输入源独立，不自动抓取 step4_sampling 的输出
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'dependencies': None,
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'constructor_kwargs': None,
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},
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{
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'step_id': 'step6_feature',
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'class_ref': Step6FeaturePanel,
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'title': '光谱特征',
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'icon': '6.png',
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'stage': '阶段二：样本数据准备',
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'display_name': '6. 光谱特征提取',
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'dependencies': {
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'deglint_img_path': ('step3', 'deglint_image', 'deglint_img_file'),
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'csv_path': ('step5_clean', 'processed_data', 'csv_file'),
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'boundary_mask_path': ('step1', 'water_mask', 'water_mask_file'),
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'glint_mask_path': ('step2', 'glint_mask', 'glint_mask_file'),
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},
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'constructor_kwargs': None,
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},
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{
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'step_id': 'step7_index',
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'class_ref': Step7IndexPanel,
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'title': '水质光谱指数计算',
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'icon': '7.png',
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'stage': '阶段二：样本数据准备',
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'display_name': '7. 水质指数计算',
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'dependencies': {
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'training_csv_path': ('step6_feature', 'training_spectra', 'training_data_widget'),
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},
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'constructor_kwargs': None,
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},
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# ═══════════════════════════════════════════════════════════════
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# 阶段三：模型构建与训练
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# ═══════════════════════════════════════════════════════════════
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{
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'step_id': 'step8_ml_train',
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'class_ref': Step8MlTrainPanel,
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'title': '机器学习建模',
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'icon': '8.png',
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'stage': '阶段三：模型构建与训练',
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'display_name': '8. 机器学习建模',
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'dependencies': {
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'training_csv_file': ('step7_index', 'training_spectra_indices', 'training_csv_file'),
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},
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'constructor_kwargs': None,
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},
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# ═══════════════════════════════════════════════════════════════
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# 阶段四：预测与成果输出
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# ═══════════════════════════════════════════════════════════════
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{
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'step_id': 'step9_ml_predict',
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'class_ref': Step9MlPredictPanel,
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'title': '机器学习预测',
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'icon': '10.png',
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'stage': '阶段四：预测与成果输出',
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'display_name': '9. 机器学习预测',
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'dependencies': {
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'models_dir': ('step8_ml_train', 'Supervised_Model_Training', 'models_dir_widget'),
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},
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'constructor_kwargs': None,
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},
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{
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'step_id': 'step10_watercolor',
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'class_ref': Step10WatercolorPanel,
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'title': '水色指数反演',
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'icon': '10.png',
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'stage': '阶段四：预测与成果输出',
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'display_name': '10. 水色指数反演',
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'dependencies': {
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'bsq_file': ('step3', 'deglint_image', 'bsq_file'),
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},
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'constructor_kwargs': None,
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},
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{
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'step_id': 'step11_map',
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'class_ref': Step11MapPanel,
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'title': '专题图生成',
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'icon': '10.png',
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'stage': '阶段四：预测与成果输出',
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'display_name': '11. 专题图生成',
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'dependencies': {
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'prediction_csv_dir_edit': ('step9_ml_predict', '9_ML_Prediction', 'prediction_csv_dir_edit'),
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'geotiff_dir_edit': ('step10_watercolor', 'WaterIndex_Images', 'geotiff_dir_edit'),
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},
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'constructor_kwargs': None,
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},
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{
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'step_id': 'step12_viz',
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'class_ref': Step12VizPanel,
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'title': '可视化',
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'icon': '9.png',
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'stage': '阶段四：预测与成果输出',
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'display_name': '12. 可视化展示',
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'dependencies': None,
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'constructor_kwargs': None,
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},
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{
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'step_id': 'step13_report',
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'class_ref': Step13ReportPanel,
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'title': '报告生成',
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'icon': '10.png',
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'stage': '阶段四：预测与成果输出',
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'display_name': '13. 分析报告生成',
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'dependencies': None,
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'constructor_kwargs': {'main_window'}, # 需要注入 main_window=self
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},
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]
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def build_step_dependencies():
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"""从 PANEL_REGISTRY 构建 step_dependencies 字典。
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Returns:
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dict: {step_id: {input_field: (dep_step, output_type, panel_attr)}}
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"""
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deps = {}
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for entry in PANEL_REGISTRY:
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if entry['dependencies']:
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deps[entry['step_id']] = entry['dependencies']
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return deps
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def build_stage_groups():
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"""从 PANEL_REGISTRY 构建阶段分组字典。
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Returns:
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dict: {stage_name: [(step_id, display_name), ...]}
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"""
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groups = {}
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for entry in PANEL_REGISTRY:
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stage = entry['stage']
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if stage not in groups:
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groups[stage] = []
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groups[stage].append((entry['step_id'], entry['display_name']))
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return groups
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def get_tab_index(step_id):
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"""根据 step_id 获取其在 PANEL_REGISTRY 中的索引（即 Tab 索引）。"""
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for i, entry in enumerate(PANEL_REGISTRY):
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if entry['step_id'] == step_id:
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return i
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return -1
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def get_step_id_by_tab_index(tab_index):
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"""根据 Tab 索引获取 step_id。"""
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if 0 <= tab_index < len(PANEL_REGISTRY):
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return PANEL_REGISTRY[tab_index]['step_id']
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return None
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def get_entry(step_id):
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"""根据 step_id 获取注册表条目。"""
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for entry in PANEL_REGISTRY:
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if entry['step_id'] == step_id:
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return entry
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return None
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