都是

def process_spec_dfs(df_list: List[pd.DataFrame], spec_names: List[str]) -> Dict:"""处理多个spec的DataFrame列表，生成按recipe分组的特征范围字典（交集优先，无交集则并集）参数：df_list: 包含各spec数据的DataFrame列表（每个df的spec统一）spec_names: 与df_list一一对应的spec名称（如["Oxremain", "OtherSpec"]）返回：目标嵌套字典，格式为 {recipe: {feature: [min, max]}}"""# 1. 初始化存储结构：key为(recipe, feature)，value为收集的[min, max]列表recipe_feature_ranges: Dict[Tuple[str, str], List[Tuple[float, float]]] = {}# 2. 遍历每个spec的DataFrame，提取并存储范围for df, spec in zip(df_list, spec_names):# 检查必要列是否存在（base_id + 至少一个*_group_min/*_group_max）required_cols = ["base_id"]feature_cols = [col for col in df.columns if col.endswith(("_group_min", "_group_max"))]if not feature_cols:raise ValueError(f"DataFrame for spec {spec} has no *_group_min/*_group_max columns")# 遍历DataFrame的每一行（每个base_id对应的特征范围）for _, row in df.iterrows():# 从base_id提取recipe（如从"C2215-dual-fin-R01#Oxremain"提取"R01"）base_id = str(row["base_id"])recipe_match = re.search(r"R\d+", base_id)  # 匹配R开头+数字的recipe（如R01、R10）if not recipe_match:raise ValueError(f"base_id {base_id} (spec: {spec}) has no valid recipe (e.g., R01)")recipe = recipe_match.group()  # 提取出的recipe（如"R01"）# 遍历当前行的所有特征（按"特征名"分组，对应min和max列）# 例：SiArc#ProcessTime_group_min 和 SiArc#ProcessTime_group_max 对应同一个特征feature_names = list(set(col.replace("_group_min", "").replace("_group_max", "") for col in feature_cols))for feature in feature_names:min_col = f"{feature}_group_min"max_col = f"{feature}_group_max"if min_col not in df.columns or max_col not in df.columns:raise ValueError(f"Feature {feature} in spec {spec} lacks min/max column")# 提取当前spec下该特征的min和max（转为float避免类型问题）current_min = float(row[min_col])current_max = float(row[max_col])# 确保min ≤ max（避免数据错误）if current_min > current_max:current_min, current_max = current_max, current_min# 将当前范围存入存储结构key = (recipe, feature)if key not in recipe_feature_ranges:recipe_feature_ranges[key] = []recipe_feature_ranges[key].append((current_min, current_max))# 3. 计算每个(recipe, feature)的最终范围（交集/并集）result: Dict[str, Dict[str, List[float]]] = {}for (recipe, feature), ranges in recipe_feature_ranges.items():# 收集所有min和maxall_mins = [r[0] for r in ranges]all_maxs = [r[1] for r in ranges]# 计算交集：最大的min ≤ 最小的max → 交集有效intersection_min = max(all_mins)intersection_max = min(all_maxs)if intersection_min <= intersection_max:final_min, final_max = intersection_min, intersection_maxelse:# 交集为空 → 取并集final_min, final_max = min(all_mins), max(all_maxs)# 组装结果字典if recipe not in result:result[recipe] = {}result[recipe][feature] = [final_min, final_max]return result