WQ_GUI/src/core/pipeline/runner.py

# -*- coding: utf-8 -*-
"""
PipelineRunner：基于 StepSpec 声明式调度 14 个 step。

设计要点：
  - StepSpec 声明 requires（ctx 字段名列表）+ produces（ctx 字段名列表）
  - 命名约定：ctx 字段名 == panel key 名 == step 形参名（全链路无翻译）
  - 步骤命名：step_id 格式为 stepN 或 stepN_suffix（无小数位），method_name 与 step_id 对齐
  - 调度顺序：按 PIPELINE_STEPS 列表顺序，requires 缺则 skip
  - 软取消：在每个 step 前检查 ctx.is_cancelled()
  - 断点续跑：spec.output_file 已落盘则跳过执行
  - 错误汇总：全流程结束后 error_summary 记录所有 step 的异常
  - 预检：run() 入口硬校验 step1 img_path；其余依赖通过智能补全 + 软警告处理
  - PipelineHalt：外层 run() 不 catch，触发循环 break，实现硬终止
  - STEP_MAP：旧 step_id → 新 step_id 双向映射，供 GUI 配置兼容使用
  - duck-typed pipeline：runner 只调 getattr(pipeline, method_name)，不强依赖类层级
"""

from __future__ import annotations
import os
import time
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Sequence

from .context import PipelineContext, STEP_MAP_OLD_TO_NEW, STEP_MAP_NEW_TO_OLD, resolve_step_id


# ============================================================
# 终止异常（外层 run() 不 catch，触发循环 break）
# ============================================================

class PipelineHalt(Exception):
    """不可恢复的错误，在 run() 循环中抛出后直接 break，不走 Exception 处理分支。

    适用场景：
      - GUI 层通过 _notify 弹窗拦截后主动抛出的硬终止信号
    """
    pass


# ============================================================
# StepSpec 声明式描述
# ============================================================

@dataclass
class StepSpec:
    """单个 step 的元信息（声明式，避免硬编码）"""
    step_id: str
    method_name: str
    requires: List[str]                                  # PipelineContext 字段名列表
    produces: List[str] = field(default_factory=list)    # 写入 ctx 的字段名列表
    enabled: bool = True
    parameter_map: Dict[str, str] = field(default_factory=dict)
    # 当 requires 中任一字段为 None 时是否跳过；默认 True（缺输入就 skip）
    skip_when_missing: bool = True
    # 备注（仅用于文档生成 / 调试输出）
    description: str = ""
    # ★ 断点续跑：产物文件路径，支持 {work_dir} 占位符（运行时解析）
    output_file: Optional[str] = None
    # ★ 预检用：需要验证磁盘文件实际存在的 ctx key 列表
    required_input_files: List[str] = field(default_factory=list)


# ============================================================
# 14 个 step 的声明表（顺序即调度顺序）
# step_id / method_name 均不含小数位，与前端显示对齐
# output_file / required_input_files 使用 {work_dir} 占位符，由 _resolve_path 展开
# ============================================================

PIPELINE_STEPS: List[StepSpec] = [
    StepSpec(
        step_id="step1", method_name="step1_water_mask",
        requires=["img_path"], produces=["water_mask_path"],
        required_input_files=["img_path"],
        output_file="{work_dir}/1_water_mask/water_mask.dat",
        description="水域掩膜生成（NDWI 或 SHP）",
    ),
    StepSpec(
        step_id="step2", method_name="step2_glint_detection",
        requires=["img_path", "water_mask_path"], produces=["glint_mask_path"],
        required_input_files=["img_path", "water_mask_path"],
        output_file="{work_dir}/2_glint/glint_mask.dat",
        description="耀斑区域检测",
    ),
    StepSpec(
        step_id="step3", method_name="step3_deglint",
        requires=["img_path", "water_mask_path", "glint_mask_path"],
        produces=["deglint_img_path"],
        required_input_files=["img_path", "water_mask_path", "glint_mask_path"],
        output_file="{work_dir}/3_deglint/deglint.bsq",
        description="耀斑去除",
    ),
    StepSpec(
        step_id="step4", method_name="step4_data_preparation",
        requires=["csv_path"], produces=["processed_csv_path"],
        required_input_files=["csv_path"],
        output_file="{work_dir}/4_processed_data/processed_data.csv",
        description="CSV 异常值清洗",
    ),
    StepSpec(
        step_id="step5", method_name="step5_spectral_extraction",
        requires=["deglint_img_path", "processed_csv_path", "csv_path", "boundary_path", "glint_mask_path"],
        produces=["training_csv_path"],
        parameter_map={
            "processed_csv_path": "csv_path",
            "csv_path": "_raw_csv_ignored",
        },
        skip_when_missing=False,
        required_input_files=["deglint_img_path", "processed_csv_path", "boundary_path", "glint_mask_path"],
        output_file="{work_dir}/5_training_spectra/training_spectra.csv",
        description="实测样本点光谱提取",
    ),
    StepSpec(
        step_id="step8", method_name="step8_water_quality_indices",
        requires=["training_csv_path"], produces=["indices_path"],
        required_input_files=["training_csv_path"],
        output_file="{work_dir}/6_water_quality_indices/water_quality_indices.csv",
        description="水质光谱指数计算（optional）",
    ),
    StepSpec(
        step_id="step7", method_name="step7_ml_modeling",
        requires=["training_csv_path"], produces=["models_dir"],
        required_input_files=["training_csv_path"],
        output_file="{work_dir}/7_Supervised_Model_Training/best_models.pkl",
        description="ML 建模（GridSearchCV / AutoML）",
    ),
    StepSpec(
        step_id="step8_non_empirical_modeling",
        method_name="step8_non_empirical_modeling",
        requires=["training_csv_path"], produces=["models_dir"],
        parameter_map={"training_csv_path": "csv_path"},
        required_input_files=["training_csv_path"],
        output_file="{work_dir}/8_Regression_Modeling/non_empirical_models.pkl",
        description="非经验统计回归",
    ),
    StepSpec(
        step_id="step9", method_name="step9_custom_regression",
        requires=["indices_path"], produces=["models_dir"],
        parameter_map={"indices_path": "csv_path"},
        required_input_files=["indices_path"],
        output_file="{work_dir}/9_Custom_Regression_Modeling/custom_regression_models.pkl",
        description="自定义回归分析",
    ),
    StepSpec(
        step_id="step10", method_name="step10_sampling",
        requires=["deglint_img_path", "water_mask_path"], produces=["sampling_csv_path"],
        required_input_files=["deglint_img_path", "water_mask_path"],
        output_file="{work_dir}/10_sampling/sampling_spectra.csv",
        description="整景密集采样点生成 + 光谱提取",
    ),
    StepSpec(
        step_id="step11_ml", method_name="step11_ml_prediction",
        requires=["sampling_csv_path", "models_dir"], produces=["prediction_csv_path"],
        required_input_files=["sampling_csv_path", "models_dir"],
        output_file="{work_dir}/11_12_13_predictions/prediction_results.csv",
        description="ML 模型预测（采样点）",
    ),
    StepSpec(
        step_id="step11", method_name="step11_non_empirical_prediction",
        requires=["sampling_csv_path", "models_dir"], produces=["prediction_dir"],
        parameter_map={"models_dir": "non_empirical_models_dir"},
        required_input_files=["sampling_csv_path", "models_dir"],
        output_file="{work_dir}/11_12_13_predictions/non_empirical_predictions",
        description="非经验模型预测",
    ),
    StepSpec(
        step_id="step12", method_name="step12_custom_regression_prediction",
        requires=["sampling_csv_path", "models_dir", "formula_csv_path"],
        produces=["prediction_dir"],
        parameter_map={"models_dir": "custom_regression_dir"},
        required_input_files=["sampling_csv_path", "models_dir", "formula_csv_path"],
        output_file="{work_dir}/11_12_13_predictions/custom_regression_predictions",
        description="自定义回归预测",
    ),
    StepSpec(
        step_id="step14", method_name="step14_distribution_map",
        requires=["prediction_csv_path", "boundary_shp_path"],
        produces=["distribution_map_path"],
        required_input_files=["prediction_csv_path", "boundary_shp_path"],
        output_file="{work_dir}/distribution_map.png",
        description="克里金插值成图",
    ),
]


# ============================================================
# PipelineRunner：执行者
# ============================================================

class PipelineRunner:
    """按 StepSpec 调度 14 个 step 方法，支持软取消 + 断点续跑 + 错误汇总。

    用法：
        ctx = PipelineContext(img_path=..., work_dir=..., user_config=config)
        runner = PipelineRunner(pipeline_instance)
        result_ctx = runner.run(ctx)              # 预检通过后开始执行
        print(result_ctx.error_summary)           # [(step_id, error_msg), ...]
    """

    def __init__(self, pipeline, steps: Optional[Sequence[StepSpec]] = None):
        self.pipeline = pipeline
        self.steps: List[StepSpec] = list(steps) if steps else list(PIPELINE_STEPS)

    # ------------------------------------------------------------------
    # 主入口
    # ------------------------------------------------------------------

    def run(self, ctx: PipelineContext, skip_list: Optional[List[str]] = None) -> PipelineContext:
        """全流程入口：智能补全 → 预检（软警告）→ 执行。

        Args:
            ctx: PipelineContext
            skip_list: 用户在 PreflightDialog 中选择忽略的 step_id 列表。
                       命中项设置 status="user_skipped"，打印醒目日志。
        """
        ctx.pipeline_start_time = time.time()
        error_summary: List[tuple[str, str]] = []
        skip_set = set(skip_list) if skip_list else set()

        # ── ★ Step1 img_path 硬校验（缺失则立即终止整个流程） ──
        if not ctx.get("img_path"):
            msg = "【全流程预检失败】缺少参考影像路径 (img_path)，流程无法启动。"
            ctx.append_log(f"[RUNNER] {msg}")
            self._notify_step("全流程", "error", msg)
            ctx.last_error = msg
            ctx.pipeline_end_time = time.time()
            return ctx

        # ── ★ 智能补全：扫描 work_dir 默认产物路径，回填 ctx ──
        self._scan_workdir_outputs(ctx)

        # ── ★ 自动补全缺失步骤：work_dir 有产物则强制开启 + 回填路径 ──
        self._auto_fill_missing_steps(ctx)

        # ── 软预检警告（不再阻断，仅记录日志）──
        self._preflight_warnings(ctx)

        # 断点续跑预扫描：ctx 已有产物则记录诊断日志
        self._restore_outputs_from_ctx(ctx)

        # ── ★ 依赖级联自动唤醒：在主循环开始前补齐所有前置缺口 ──
        self._resolve_dependencies(ctx)

        for spec in self.steps:
            # ── 软取消 ──
            if ctx.is_cancelled():
                ctx.append_log(f"[RUNNER] 收到取消信号，提前终止 @ {spec.step_id}")
                break

            # ── disabled 跳过（locked_steps 不受此约束）──
            if not spec.enabled and spec.step_id not in ctx.locked_steps:
                ctx.status[spec.step_id] = "skipped"
                ctx.append_log(f"[RUNNER] {spec.step_id} 标记为 disabled，跳过")
                continue

            # ── ★ 用户强制跳过（PreflightDialog 勾选） ──
            if spec.step_id in skip_set:
                ctx.status[spec.step_id] = "user_skipped"
                ctx.append_log(
                    f"\n{'='*60}\n"
                    f"  ⚠ 用户强制跳过: {spec.step_id}（{spec.description}）\n"
                    f"  原因：用户在预检弹窗中勾选「忽略」，已确认跳过\n"
                    f"{'='*60}\n"
                )
                self._notify_step(spec.step_id, "skipped", "用户强制跳过（预检弹窗）")
                continue

            # ── 依赖缺失检查 ──
            if spec.skip_when_missing:
                missing = [k for k in spec.requires if not ctx.get(k)]
                if missing:
                    # ── ★ 智能补全的步骤：work_dir 有产物，但 requires 仍缺失（罕见），报 warning 不跳过
                    if spec.step_id in ctx.locked_steps:
                        ctx.append_log(
                            f"[RUNNER] ⚠ {spec.step_id} 已锁定但 requires 仍缺失 {missing}，"
                            "尝试执行（可能因依赖前置步骤失败）"
                        )
                    else:
                        ctx.status[spec.step_id] = "skipped"
                        reason = f"缺少必要的上下文参数，自动跳过: {missing}"
                        ctx.append_log(f"[RUNNER] {spec.step_id} {reason}")
                        self._notify_step(spec.step_id, "skipped", reason)
                        continue

            # ── ★ 断点续跑：产物文件已存在则跳过 ──
            resolved_path = self._resolve_path(spec.output_file, ctx)
            if resolved_path and os.path.exists(resolved_path):
                ctx.status[spec.step_id] = "skipped"
                reason = f"产物已存在，跳过: {resolved_path}"
                ctx.append_log(f"[RUNNER] {spec.step_id} {reason}")
                self._notify_step(spec.step_id, "skipped", reason)
                self._restore_ctx_from_output(spec, resolved_path, ctx)
                continue

            # ── 执行（正常路径） ──
            try:
                self._invoke(spec, ctx)
            except PipelineHalt:
                # ★ PipelineHalt 不走 error_summary，触发立即 break
                ctx.append_log(f"[RUNNER] PipelineHalt 硬终止 @ {spec.step_id}")
                self._notify_step(spec.step_id, "error", "预检失败，硬终止")
                break
            except Exception as exc:
                ctx.status[spec.step_id] = "error"
                error_summary.append((spec.step_id, str(exc)))
                ctx.last_error = f"{spec.step_id}: {exc!r}"
                ctx.append_log(f"[RUNNER] {spec.step_id} 异常: {exc!r}")
                self._notify_step(spec.step_id, "error", str(exc))
                # ★ 任意 Exception 均立即 break，不再执行后续步骤
                break

        ctx.pipeline_end_time = time.time()
        ctx.error_summary = error_summary
        return ctx

    # ------------------------------------------------------------------
    # ★ 智能补全：工作目录产物扫描
    # ------------------------------------------------------------------

    def _scan_workdir_outputs(self, ctx: PipelineContext) -> None:
        """扫描 work_dir 下所有步骤的默认产物路径，若存在则回填 ctx。

        利用 spec.output_file 的 {work_dir} 占位符，展开为实际绝对路径。
        存在则写入对应的 ctx 字段（produces），供后续步骤直接使用。
        已在 ctx 中有值的字段不会被覆盖。
        """
        work_dir = ctx.get("work_dir") or ""
        if not work_dir:
            return

        for spec in self.steps:
            if not spec.produces:
                continue
            for produce_key in spec.produces:
                if ctx.get(produce_key):
                    continue  # 已有人工填写的值，不覆盖
                resolved = self._resolve_path(spec.output_file, ctx)
                if resolved and os.path.exists(resolved):
                    ctx.set(produce_key, resolved)
                    ctx.append_log(
                        f"[AUTO_FILL] 检测到已有产物，回填 {produce_key} = {resolved}"
                    )

    # ------------------------------------------------------------------
    # ★ 智能补全：强制开启被静默跳过的步骤
    # ------------------------------------------------------------------

    def _auto_fill_missing_steps(self, ctx: PipelineContext) -> None:
        """检查所有 disabled 步骤。

        若某步骤的 output_file 已在 work_dir 落盘（断点续跑），
        说明该步骤之前已完成但被用户在 GUI 中禁用了。
        此时系统自动重开启该步骤（forced=True），并将其加入 locked_steps。

        同时，将已落盘的产物路径回填到对应的 ctx 字段，
        确保下游步骤能正常拿到输入。

        阻断性缺失（step1 img_path）已在 run() 入口硬校验，此处不处理。
        """
        newly_locked: List[str] = []

        for spec in self.steps:
            if spec.enabled:
                continue  # 用户主动开启的步骤不受影响
            skip_set = getattr(ctx, '_skip_set', set())
            if spec.step_id in skip_set:
                continue  # 用户在 PreflightDialog 中手动忽略的步骤不自动补全

            resolved = self._resolve_path(spec.output_file, ctx)
            if resolved and os.path.exists(resolved):
                # ── 该步骤已有产物但被禁用 → 自动开启 ──
                spec.enabled = True
                ctx.locked_steps.append(spec.step_id)
                newly_locked.append(spec.step_id)

                # 回填所有产物字段到 ctx
                for produce_key in spec.produces:
                    if not ctx.get(produce_key):
                        ctx.set(produce_key, resolved)
                        ctx.append_log(
                            f"[AUTO_FILL] 强制开启并回填 {spec.step_id} 产物 {produce_key} = {resolved}"
                        )

                ctx.append_log(
                    f"\n{'='*60}\n"
                    f"  ⚡ 智能补全：步骤 {spec.step_id}（{spec.description}）\n"
                    f"  原因：该步骤在 work_dir 中已有产物但被您在 GUI 中禁用了。\n"
                    f"  操作：系统已自动开启该步骤，产物路径已回填。\n"
                    f"  注意：运行期间该步骤已被锁定，您无法临时关闭。\n"
                    f"{'='*60}\n"
                )

        if newly_locked:
            self._notify_step(
                "全流程",
                "info",
                f"智能补全已自动开启 {len(newly_locked)} 个步骤：{newly_locked}"
            )

    # ------------------------------------------------------------------
    # ★ 依赖级联自动唤醒引擎
    # ------------------------------------------------------------------

    def _resolve_dependencies(self, ctx: PipelineContext) -> None:
        """依赖追溯循环：遍历所有 enabled 步骤，强制唤醒缺失前置。

        核心逻辑：
          - 遍历当前 enabled=True 的步骤，检查其 requires
          - 若所需 key 在 ctx 中不存在，则向上寻找 produces 该 key 的前置 Step
          - 将该前置 Step 强制设为 enabled=True（加入 locked_steps）
          - 递归执行，直到所有前置缺口都被强制补齐
          - 已存在的产物文件自动回填 ctx
        """
        # 构建 produces→step_id 反查表（仅关注 enabled 或潜在的前置步骤）
        produce_to_step: Dict[str, StepSpec] = {}
        for spec in self.steps:
            for key in spec.produces:
                produce_to_step[key] = spec

        woke_up: List[str] = []
        changed = True

        while changed:
            changed = False
            for spec in self.steps:
                if not spec.enabled:
                    continue

                for required_key in spec.requires:
                    # ctx 已有值 → 无需追溯
                    if ctx.get(required_key):
                        continue

                    # 磁盘文件是否存在（work_dir 产物已落盘但 ctx 未回填的情况）
                    resolved = self._resolve_output_for_key(required_key, ctx)
                    if resolved and os.path.exists(resolved):
                        ctx.set(required_key, resolved)
                        continue

                    # 缺少且无磁盘产物 → 追溯 produces 者
                    if required_key not in produce_to_step:
                        continue

                    provider = produce_to_step[required_key]
                    if provider.enabled:
                        continue  # 已开启但尚未执行（会在主循环中处理）

                    # 强制唤醒
                    provider.enabled = True
                    if provider.step_id not in ctx.locked_steps:
                        ctx.locked_steps.append(provider.step_id)
                    woke_up.append(provider.step_id)
                    ctx.append_log(
                        f"[INFO] 因下游依赖需求，自动唤醒并执行步骤: {provider.step_id}"
                    )

                    # 递归：检查新开启步骤自身的前置是否也缺失
                    changed = True

        if woke_up:
            detail = "、".join(woke_up)
            ctx.append_log(
                f"[RUNNER] ★ 依赖级联自动唤醒已完成，共开启 {len(woke_up)} 个步骤：{detail}"
            )
            self._notify_step(
                "全流程", "info",
                f"依赖级联自动唤醒 {len(woke_up)} 个步骤：{woke_up}"
            )
            # 扫描新开启步骤的 work_dir 产物，回填 ctx
            for spec in self.steps:
                if spec.step_id in woke_up:
                    self._scan_single_step_outputs(spec, ctx)

    def _resolve_output_for_key(
        self, produce_key: str, ctx: PipelineContext
    ) -> Optional[str]:
        """根据 produces key 查找对应步骤的 output_file 并展开路径。"""
        for spec in self.steps:
            if produce_key in spec.produces:
                return self._resolve_path(spec.output_file, ctx)
        return None

    def _scan_single_step_outputs(
        self, spec: StepSpec, ctx: PipelineContext
    ) -> None:
        """扫描单个步骤的 work_dir 产物，回填 ctx（不覆盖已有值）。"""
        if not spec.produces:
            return
        for produce_key in spec.produces:
            if ctx.get(produce_key):
                continue
            resolved = self._resolve_path(spec.output_file, ctx)
            if resolved and os.path.exists(resolved):
                ctx.set(produce_key, resolved)
                ctx.append_log(
                    f"[AUTO_FILL] 依赖唤醒后检测到产物，回填 {produce_key} = {resolved}"
                )

    # ------------------------------------------------------------------
    # 软预检警告（不再阻断）
    # ------------------------------------------------------------------

    def _preflight_warnings(self, ctx: PipelineContext) -> None:
        """软预检警告：遍历所有步骤，检测可预见的运行时跳过。

        所有缺失均以 warning 记录日志，不抛异常，不阻止执行。
        GUI 层可通过回调函数 _notify_step 向用户展示警告列表。
        """
        warnings: List[str] = []

        for spec in self.steps:
            if not spec.enabled:
                continue

            # ── Step4 csv_path 缺失警告 ──
            if spec.step_id == "step4":
                if not ctx.get("csv_path"):
                    warnings.append(
                        f"[{spec.step_id}] 缺少实测水质数据 (csv_path)，"
                        "步骤 5-9 将被自动跳过"
                    )

            # ── 磁盘文件缺失警告（已填充 ctx 但文件实际不存在）──
            for ctx_key in spec.required_input_files:
                value = ctx.get(ctx_key)
                if not value:
                    continue
                if not os.path.exists(value):
                    warnings.append(
                        f"[{spec.step_id}] 磁盘文件缺失（但 ctx 已回填）: {ctx_key} = {value}"
                    )

        if warnings:
            detail = "\n".join(f"  - {w}" for w in warnings)
            ctx.append_log(
                f"[RUNNER] 【软预检警告】（流程将继续执行，缺失项将被自动跳过）\n{detail}"
            )
            self._notify_step("全流程", "warning", f"预检警告：{len(warnings)} 项\n{detail}")

    # ------------------------------------------------------------------
    # 单步调用
    # ------------------------------------------------------------------

    def _invoke(self, spec: StepSpec, ctx: PipelineContext) -> None:
        """调一个 step 方法：ctx 路径 → 形参；产出 → ctx 字段。"""
        ctx.append_log(
            f"[DEBUG] Step {spec.step_id} requires: {spec.requires}, "
            f"actual ctx data: {[ctx.get(k) for k in spec.requires]}"
        )
        method = getattr(self.pipeline, spec.method_name, None)
        if method is None:
            ctx.append_log(f"[RUNNER] 步骤方法缺失: {spec.method_name}（跳过）")
            ctx.status[spec.step_id] = "skipped"
            return

        # 1) 把 ctx 路径作为形参注入
        kwargs: Dict[str, Any] = {}
        for ctx_key in spec.requires:
            param_name = spec.parameter_map.get(ctx_key, self._default_param_name(ctx_key))
            kwargs[param_name] = ctx.get(ctx_key)

        # 2) 允许用户在 ctx.user_config[step_id] 覆盖/补充（非空值才覆盖）
        user_overrides = ctx.user_config.get(spec.step_id) or {}
        if isinstance(user_overrides, dict):
            for k, v in user_overrides.items():
                if v is not None and v != "":
                    kwargs[k] = v

        # 3) 状态置 start
        ctx.append_log(
            f"[RUNNER] -> {spec.method_name}({list(kwargs.keys())})"
        )
        ctx.status[spec.step_id] = "start"
        self._notify_step(spec.step_id, "start", spec.method_name)

        # 4) 执行（外层 run() 统一捕获异常）
        t0 = time.time()
        result = method(**kwargs)
        ctx.status[spec.step_id] = "completed"
        ctx.step_timings[spec.step_id] = time.time() - t0

        # 5) 产出收割
        self._harvest(spec, result, ctx)
        self._notify_step(
            spec.step_id, "completed",
            str(result)[:200] if result is not None else "",
        )

    # ------------------------------------------------------------------
    # 产出收割
    # ------------------------------------------------------------------

    def _harvest(self, spec: StepSpec, result: Any, ctx: PipelineContext) -> None:
        """把 step 方法返回值灌入 ctx 的 produces 字段。"""
        if not spec.produces:
            return
        if isinstance(result, dict):
            for produce_key in spec.produces:
                if produce_key in result:
                    ctx.set(produce_key, result[produce_key])
        elif result is not None:
            ctx.set(spec.produces[0], result)

    # ------------------------------------------------------------------
    # 断点续跑辅助
    # ------------------------------------------------------------------

    def _resolve_path(
        self, template: Optional[str], ctx: PipelineContext
    ) -> Optional[str]:
        """解析模板中的 {work_dir} 占位符，返回展开后的绝对路径或 None。"""
        if not template:
            return None
        work_dir = ctx.get("work_dir") or ""
        try:
            return template.format(work_dir=work_dir)
        except (KeyError, ValueError):
            return template

    def _restore_outputs_from_ctx(self, ctx: PipelineContext) -> None:
        """诊断日志：记录 ctx 中已有的非 None 产物。"""
        for spec in self.steps:
            if not (spec.enabled and spec.produces):
                continue
            for key in spec.produces:
                val = ctx.get(key)
                if val:
                    ctx.append_log(
                        f"[RUNNER] 断点续跑检测: {spec.step_id} 已有 {key} = {val}"
                    )

    def _restore_ctx_from_output(
        self, spec: StepSpec, resolved_path: str, ctx: PipelineContext
    ) -> None:
        """断点跳过时：将已存在的 output_file 写回 ctx 所有 produces 字段，供下游使用。

        接力棒断链修复：遍历 spec.produces 逐一注册，不遗漏任何下游可能依赖的 key。
        """
        if not spec.produces:
            return
        for produce_key in spec.produces:
            ctx.set(produce_key, resolved_path)

    # ------------------------------------------------------------------
    # 工具
    # ------------------------------------------------------------------

    @staticmethod
    def _default_param_name(ctx_key: str) -> str:
        """默认原样返回 ctx 键名作为形参名。特殊缩写由 parameter_map 显式处理。"""
        return ctx_key

    def _notify_step(self, step_id: str, status: str, message: str) -> None:
        """通过 pipeline.callback 通知 GUI 当前步骤状态。"""
        notify = getattr(self.pipeline, "_notify", None)
        if callable(notify):
            try:
                notify(step_id, status, message)
            except Exception:
                pass