NEP框架重构01select

src/workflow.py

@@ -185,7 +185,58 @@ class Workflow:
                 # Step: 01.select
                 # ==========================
                 elif step_name == "01.select":
-                    # 可以在这里也加上 StateTracker 逻辑
+                    step_dir = os.path.join(iter_path, "01.select")
-                    pass
+                    task_id_select = f"{iter_name}.01.select"
 
-                    # ... (后续步骤类似，暂时省略)
+                    if not self.tracker.is_done(task_id_select):
+                        self.logger.info(f"=== Step: 01.select ({step_conf.get('method', 'distance')}) ===")
+                        os.makedirs(step_dir, exist_ok=True)
+
+                        # 1. 准备 dump.xyz (软链接)
+                        # 优先使用上一这步记录的路径，如果没有则尝试推断
+                        dump_src = getattr(self, 'last_dump_path', None)
+                        if not dump_src:
+                            # 尝试推断：iter_XX/00.md/production/dump.xyz
+                            dump_src = os.path.join(iter_path, "00.md", "production", "dump.xyz")
+
+                        if os.path.exists(dump_src):
+                            dst_dump = os.path.join(step_dir, "dump.xyz")
+                            if os.path.exists(dst_dump): os.remove(dst_dump)
+                            # 使用绝对路径建立软链
+                            os.symlink(os.path.abspath(dump_src), dst_dump)
+                        else:
+                            self.logger.error(f"Source dump.xyz not found: {dump_src}")
+                            return
+
+                        # 2. 准备 nep.txt
+                        shutil.copy(self.current_nep_pot, os.path.join(step_dir, "nep.txt"))
+
+                        # 3. 准备 train.xyz
+                        # 逻辑：第一轮用 model.xyz，之后用 self.current_train_set
+                        if iter_id == 0:
+                            # 第一轮：找同轮次 00.md 下的 model.xyz
+                            model_xyz_src = os.path.join(iter_path, "00.md", "model.xyz")
+                            if os.path.exists(model_xyz_src):
+                                shutil.copy(model_xyz_src, os.path.join(step_dir, "train.xyz"))
+                            else:
+                                self.logger.error("model.xyz not found for initial train.xyz")
+                                return
+                        else:
+                            # 后续轮次
+                            if os.path.exists(self.current_train_set):
+                                shutil.copy(self.current_train_set, os.path.join(step_dir, "train.xyz"))
+                            else:
+                                self.logger.error(f"Previous train set missing: {self.current_train_set}")
+                                return
+
+                        # 4. 执行筛选逻辑
+                        method = step_conf.get('method', 'distance')
+                        params = step_conf.get('params', [0.01, 60, 120])  # [threshold, min, max]
+                        kit_path = self.machine.config['paths'].get('gpumdkit', 'gpumdkit.sh')
+
+                        # === 分支 A: 按距离筛选 (Loop) ===
+                        if method == "distance":
+                            threshold = params[0]
+                            target_min, target_max = params[1], params[2]
+                            step_size = 0.001
+                        self.logger.info("Skipping Select (Already Done).")