services/step2-5：打通前四个预处理步骤的真实后端独立服务
新增 src/new/services/{step2,step3,step4,step5}_service.py 四个独立后端服务：
This commit is contained in:
@ -293,7 +293,7 @@ def smoke_e2e():
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"mode=ndwi" in log_text,
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"→ 找 ['mode=ndwi']")
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# ---- 切到 step2（真实 view，service 仍走占位） ----
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# ---- 切到 step2（真实 view + 真实 service） ----
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win.nav_list.setCurrentRow(1)
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view_step2 = win._views.get("step2")
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report("L3", "_views['step2'] 是真实 Step2View（已迁移）",
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@ -310,8 +310,8 @@ def smoke_e2e():
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log_text = win.log_text.toPlainText()
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report("L3", "step2 真实 view 派发后日志含 Router 收到 step2 请求",
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"收到 step2 请求" in log_text)
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report("L3", "step2 service 走占位：日志含 Service… not_implemented",
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"not_implemented" in log_text,
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report("L3", "step2 真实 service 已迁移：空 img_path 触发 Service✗ 错误分支",
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"[Service✗]" in log_text and "execute_step2" in log_text,
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f"log 片段：{log_text[-200:]!r}")
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win.close()
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@ -68,38 +68,38 @@ ROUTES = [
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"service_module": "src.new.services.step1_service",
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"service_func": "execute_step1",
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},
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# -------- 业务路由（view 全部已迁移；service 仍走占位） --------
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# -------- 业务路由（前 4 个预处理步骤已迁移到真实 service） --------
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{
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"id": "step2",
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"name": "2. 耀斑检测",
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"view_module": "src.new.views.step2_view",
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"view_class": "Step2View",
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"service_module": "src.new.services.placeholder_service",
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"service_func": "execute_placeholder",
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"service_module": "src.new.services.step2_service",
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"service_func": "execute_step2",
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},
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{
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"id": "step3",
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"name": "3. 耀斑去除",
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"view_module": "src.new.views.step3_view",
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"view_class": "Step3View",
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"service_module": "src.new.services.placeholder_service",
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"service_func": "execute_placeholder",
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"service_module": "src.new.services.step3_service",
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"service_func": "execute_step3",
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},
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{
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"id": "step4",
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"name": "4. 采样点布设",
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"view_module": "src.new.views.step4_view",
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"view_class": "Step4View",
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"service_module": "src.new.services.placeholder_service",
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"service_func": "execute_placeholder",
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"service_module": "src.new.services.step4_service",
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"service_func": "execute_step4",
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},
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{
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"id": "step5",
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"name": "5. 数据清洗",
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"view_module": "src.new.views.step5_view",
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"view_class": "Step5View",
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"service_module": "src.new.services.placeholder_service",
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"service_func": "execute_placeholder",
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"service_module": "src.new.services.step5_service",
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"service_func": "execute_step5",
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},
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{
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"id": "step6",
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@ -233,7 +233,7 @@ class MainView(QMainWindow):
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self._build_routes()
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self._log("[Boot] MainView 初始化完成")
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self._log(f"[Boot] 已注册 {len(ROUTES)} 条路由（13 个 view 全部真实，service 仍走占位）")
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self._log(f"[Boot] 已注册 {len(ROUTES)} 条路由（13 个 view 全部真实，前 5 个步骤 service 已迁移）")
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# ------------------------------------------------------------------
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# UI 布局：左侧导航 + 右侧 stacked + 底部日志
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170
src/new/services/step2_service.py
Normal file
170
src/new/services/step2_service.py
Normal file
@ -0,0 +1,170 @@
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# -*- coding: utf-8 -*-
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"""
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Step2 后端计算服务（耀斑区域识别）
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====================================
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纯计算函数——绝对不引用 PyQt、绝对不引用 main_view、绝对不读写全局变量。它只：
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1. 从 ``config`` 字典读取参数；
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2. 调用旧版 ``GlintDetectionStep.run`` 执行耀斑检测；
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3. 返回结果字典 ``{status, output_path, message, mode}``。
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调用入口（由 main_view 在后台 QThread 中调用）：
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execute_step2({
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"img_path": "D:/ref.bsq", # 输入影像
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"glint_wave": 750.0, # 耀斑检测波长 (nm)
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"method": "otsu", # otsu/zscore/percentile/iqr/adaptive/multi_band
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"max_area": 50, # 最大连通域面积（可选，0 表示不过滤）
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"buffer_size": 10, # 岸边缓冲区大小（可选，0 表示不设置）
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"water_mask_path": "D:/mask.dat", # 水域掩膜（可选）
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"output_path": "D:/severe_glint_area.dat", # 耀斑掩膜输出
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"work_dir": "D:/workspace", # 工作目录（main_view 注入）
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})
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返回字典字段：
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* ``status`` : "completed" | "skipped" | "error"
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* ``output_path`` : 生成的 .dat 耀斑掩膜文件路径（失败时为 None）
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* ``message`` : 人类可读说明
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* ``mode`` : "ndwi" | "otsu" | "zscore" | ...（实际调用的方法，便于 UI 提示）
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注意：与 step1_service 一致，``status`` 沿用 "completed" 语义（main_view._on_step_done
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据此走 Service✓ 成功分支），对应用户契约中的 "success"。
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"""
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from __future__ import annotations
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from pathlib import Path
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from typing import Any, Dict
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from src.core.steps.glint_detection_step import GlintDetectionStep
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def _resolve_glint_dir(output_path: str | None, work_dir: str) -> Path:
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"""根据 output_path / work_dir 计算耀斑检测输出目录"""
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if output_path:
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return Path(output_path).parent
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return Path(work_dir) / "2_Glint_Detection"
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def _clean_int_param(value: Any, default: int = 0) -> int | None:
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"""SpinBox 的 0 值表示"不设置"，转 None 透传给底层"""
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if value is None:
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return default
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try:
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v = int(value)
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except (TypeError, ValueError):
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return default
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return v if v > 0 else None
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def execute_step2(config: Dict[str, Any]) -> Dict[str, Any]:
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"""Step 2 后端计算入口——纯函数
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Args:
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config: 由前端 view.get_config() 序列化、再经 main_view 注入 work_dir 的字典
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Returns:
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标准结果字典 ``{status, output_path, message, mode}``
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"""
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# ---------- 入参规整 ----------
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img_path = config.get("img_path")
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glint_wave = float(config.get("glint_wave", 750.0))
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method = str(config.get("method", "otsu")).lower()
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enabled = bool(config.get("enabled", True))
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water_mask_path = config.get("water_mask_path")
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output_path = config.get("output_path")
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work_dir = config.get("work_dir") or "."
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glint_dir = _resolve_glint_dir(output_path, work_dir)
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# ---------- 提前失败检查 ----------
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if not enabled:
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return {
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"status": "skipped",
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"output_path": None,
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"message": "用户禁用此步骤（enabled=False）",
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"mode": method,
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}
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if not img_path:
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return {
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"status": "error",
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"output_path": None,
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"message": "未提供输入影像路径（img_path）",
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"mode": method,
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}
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if not Path(img_path).exists():
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return {
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"status": "error",
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"output_path": None,
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"message": f"输入影像不存在: {img_path}",
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"mode": method,
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}
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# ---------- 构建底层 kwargs（可选字段：None 一律不传，让底层走默认） ----------
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kwargs: Dict[str, Any] = {
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"img_path": img_path,
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"glint_wave": glint_wave,
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"method": method,
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"water_mask_path": water_mask_path,
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"glint_dir": glint_dir,
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"output_path": output_path,
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"callback": None, # 日志由 main_view 统一接管
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}
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max_area = _clean_int_param(config.get("max_area"))
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if max_area is not None:
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kwargs["max_area"] = max_area
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buffer_size = _clean_int_param(config.get("buffer_size"))
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if buffer_size is not None:
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kwargs["buffer_size"] = buffer_size
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# multi_band 专用参数（如有）
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if method == "multi_band":
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for key in ("multi_band_waves", "sub_method", "weights"):
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if config.get(key) is not None:
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kwargs[key] = config[key]
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# ---------- 执行（包一层 try/except 把异常转 dict，避免炸线程） ----------
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try:
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result_path = GlintDetectionStep.run(**kwargs)
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except FileNotFoundError as e:
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return {
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"status": "error",
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"output_path": None,
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"message": f"文件不存在: {e}",
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"mode": method,
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}
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except ValueError as e:
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return {
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"status": "error",
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"output_path": None,
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"message": f"参数错误: {e}",
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"mode": method,
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}
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except Exception as e: # noqa: BLE001 —— service 层兜底捕获所有
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return {
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"status": "error",
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"output_path": None,
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"message": f"{type(e).__name__}: {e}",
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"mode": method,
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}
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# ---------- 成功路径 ----------
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p = Path(result_path)
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if not p.exists():
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return {
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"status": "error",
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"output_path": None,
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"message": f"GlintDetectionStep.run 未生成文件: {result_path}",
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"mode": method,
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}
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return {
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"status": "completed",
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"output_path": str(p).replace("\\", "/"),
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"message": f"耀斑掩膜已生成: {p.name}",
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"mode": method,
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}
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207
src/new/services/step3_service.py
Normal file
207
src/new/services/step3_service.py
Normal file
@ -0,0 +1,207 @@
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# -*- coding: utf-8 -*-
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"""
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Step3 后端计算服务（耀斑去除）
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====================================
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纯计算函数——绝对不引用 PyQt、绝对不引用 main_view、绝对不读写全局变量。它只：
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1. 从 ``config`` 字典读取参数；
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2. 调用旧版 ``GlintRemovalStep.run`` 执行去耀斑（4 种方法：Goodman/Kutser/Hedley/SUGAR）；
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3. 返回结果字典 ``{status, output_path, message, mode}``。
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调用入口（由 main_view 在后台 QThread 中调用）：
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execute_step3({
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"img_path": "D:/ref.bsq", # 输入影像（去耀斑前）
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"method": "goodman", # goodman/kutser/hedley/sugar
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"enabled": True,
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"interpolate_zeros": False, # 是否先做 0 值像素插值
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"interpolation_method": "bilinear",
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"water_mask_path": "D:/mask.dat", # 水域掩膜（可选）
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"output_path": "D:/deglint_image.bsq",
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# 方法专属参数（按 method 任选一组）
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"nir_lower": 65, "nir_upper": 91, "goodman_A": 1.9e-5, "goodman_B": 0.1, # goodman
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"oxy_band": 38, "lower_oxy": 36, "upper_oxy": 49, "nir_band": 47, # kutser
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"hedley_nir_band": 47, # hedley
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"sugar_iter": 3, "sugar_sigma": 1.0, "sugar_estimate_background": True,
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"sugar_glint_mask_method": "cdf", "sugar_termination_thresh": 20.0,
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"sugar_bounds": [(1, 2)], # sugar
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"work_dir": "D:/workspace", # 工作目录（main_view 注入）
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})
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返回字典字段：
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* ``status`` : "completed" | "skipped" | "error"
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* ``output_path`` : 生成的 .bsq 去耀斑影像路径（失败时为 None）
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* ``message`` : 人类可读说明
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* ``mode`` : 实际调用的去耀斑方法名，便于 UI 提示
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"""
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from __future__ import annotations
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from pathlib import Path
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from typing import Any, Dict
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from src.core.steps.glint_removal_step import GlintRemovalStep
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def _resolve_dirs(output_path: str | None, work_dir: str) -> tuple[Path, Path]:
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"""根据 output_path / work_dir 计算 (deglint_dir, water_mask_dir)"""
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if output_path:
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deglint_dir = Path(output_path).parent
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else:
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deglint_dir = Path(work_dir) / "3_Deglint"
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water_mask_dir = Path(work_dir) / "1_water_mask"
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return deglint_dir, water_mask_dir
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def _normalize_method(method: str) -> str:
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"""方法名标准化（与 GlintRemovalStep.run 内部规则保持一致）"""
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raw = str(method).lower()
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if "kutser" in raw:
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return "kutser"
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if "goodman" in raw:
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return "goodman"
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if "hedley" in raw:
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return "hedley"
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if "sugar" in raw:
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return "sugar"
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return raw
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def _build_method_kwargs(method: str, config: Dict[str, Any]) -> Dict[str, Any]:
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"""按 method 从 config 中抽取对应的方法专属参数"""
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if method == "goodman":
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return {
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"nir_lower": int(config.get("nir_lower", 65)),
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"nir_upper": int(config.get("nir_upper", 91)),
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"goodman_A": float(config.get("goodman_A", 0.000019)),
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"goodman_B": float(config.get("goodman_B", 0.1)),
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}
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if method == "kutser":
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return {
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"oxy_band": int(config.get("oxy_band", 38)),
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"lower_oxy": int(config.get("lower_oxy", 36)),
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"upper_oxy": int(config.get("upper_oxy", 49)),
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"nir_band": int(config.get("nir_band", 47)),
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}
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if method == "hedley":
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return {
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"hedley_nir_band": int(config.get("hedley_nir_band", 47)),
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}
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if method == "sugar":
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bounds = config.get("sugar_bounds")
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if bounds is None or not isinstance(bounds, (list, tuple)):
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bounds = [(1, 2)]
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return {
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"sugar_iter": int(config.get("sugar_iter", 3)),
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"sugar_sigma": float(config.get("sugar_sigma", 1.0)),
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"sugar_estimate_background": bool(config.get("sugar_estimate_background", True)),
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"sugar_glint_mask_method": str(config.get("sugar_glint_mask_method", "cdf")),
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"sugar_termination_thresh": float(config.get("sugar_termination_thresh", 20.0)),
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"sugar_bounds": bounds,
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}
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return {}
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def execute_step3(config: Dict[str, Any]) -> Dict[str, Any]:
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"""Step 3 后端计算入口——纯函数
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Args:
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config: 由前端 view.get_config() 序列化、再经 main_view 注入 work_dir 的字典
|
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Returns:
|
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标准结果字典 ``{status, output_path, message, mode}``
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"""
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# ---------- 入参规整 ----------
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img_path = config.get("img_path")
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raw_method = config.get("method", "goodman")
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method = _normalize_method(raw_method)
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enabled = bool(config.get("enabled", True))
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interpolate_zeros = bool(config.get("interpolate_zeros", False))
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interpolation_method = str(config.get("interpolation_method", "bilinear"))
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water_mask_path = config.get("water_mask_path")
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output_path = config.get("output_path")
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work_dir = config.get("work_dir") or "."
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deglint_dir, water_mask_dir = _resolve_dirs(output_path, work_dir)
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# ---------- 提前失败检查 ----------
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if not enabled:
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return {
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"status": "skipped",
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"output_path": img_path,
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"message": "用户禁用此步骤（enabled=False），保留原始影像",
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"mode": method,
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}
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if not img_path:
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return {
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"status": "error",
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"output_path": None,
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"message": "未提供输入影像路径（img_path）",
|
||||
"mode": method,
|
||||
}
|
||||
if not Path(img_path).exists():
|
||||
return {
|
||||
"status": "error",
|
||||
"output_path": None,
|
||||
"message": f"输入影像不存在: {img_path}",
|
||||
"mode": method,
|
||||
}
|
||||
|
||||
# ---------- 构建底层 kwargs ----------
|
||||
method_kwargs = _build_method_kwargs(method, config)
|
||||
kwargs: Dict[str, Any] = {
|
||||
"img_path": img_path,
|
||||
"method": method,
|
||||
"water_mask": water_mask_path,
|
||||
"interpolate_zeros": interpolate_zeros,
|
||||
"interpolation_method": interpolation_method,
|
||||
"deglint_dir": deglint_dir,
|
||||
"water_mask_dir": water_mask_dir,
|
||||
"output_path": output_path,
|
||||
"callback": None, # 日志由 main_view 统一接管
|
||||
}
|
||||
kwargs.update(method_kwargs)
|
||||
|
||||
# ---------- 执行（包一层 try/except 把异常转 dict，避免炸线程） ----------
|
||||
try:
|
||||
result_path = GlintRemovalStep.run(**kwargs)
|
||||
except FileNotFoundError as e:
|
||||
return {
|
||||
"status": "error",
|
||||
"output_path": None,
|
||||
"message": f"文件不存在: {e}",
|
||||
"mode": method,
|
||||
}
|
||||
except ValueError as e:
|
||||
return {
|
||||
"status": "error",
|
||||
"output_path": None,
|
||||
"message": f"参数错误: {e}",
|
||||
"mode": method,
|
||||
}
|
||||
except Exception as e: # noqa: BLE001 —— service 层兜底捕获所有
|
||||
return {
|
||||
"status": "error",
|
||||
"output_path": None,
|
||||
"message": f"{type(e).__name__}: {e}",
|
||||
"mode": method,
|
||||
}
|
||||
|
||||
# ---------- 成功路径 ----------
|
||||
p = Path(result_path)
|
||||
if not p.exists():
|
||||
return {
|
||||
"status": "error",
|
||||
"output_path": None,
|
||||
"message": f"GlintRemovalStep.run 未生成文件: {result_path}",
|
||||
"mode": method,
|
||||
}
|
||||
|
||||
return {
|
||||
"status": "completed",
|
||||
"output_path": str(p).replace("\\", "/"),
|
||||
"message": f"去耀斑影像已生成: {p.name}",
|
||||
"mode": method,
|
||||
}
|
||||
146
src/new/services/step4_service.py
Normal file
146
src/new/services/step4_service.py
Normal file
@ -0,0 +1,146 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Step4 后端计算服务（采样点布设）
|
||||
====================================
|
||||
|
||||
纯计算函数——绝对不引用 PyQt、绝对不引用 main_view、绝对不读写全局变量。它只：
|
||||
|
||||
1. 从 ``config`` 字典读取参数；
|
||||
2. 调用旧版 ``PredictionStep.generate_sampling_points`` 在去耀斑影像上按规则
|
||||
生成采样点并提取平均光谱（自适应 / 固定间隔）；
|
||||
3. 返回结果字典 ``{status, output_path, message, mode}``。
|
||||
|
||||
调用入口（由 main_view 在后台 QThread 中调用）：
|
||||
|
||||
execute_step4({
|
||||
"deglint_img_path": "D:/deglint_image.bsq", # 去耀斑影像
|
||||
"water_mask_path": "D:/water_mask.dat", # 水域掩膜（可选）
|
||||
"interval": 50, # 采样点间隔 (像素)
|
||||
"sample_radius": 5, # 采样半径 (像素)
|
||||
"chunk_size": 1000, # 分块大小（控制内存）
|
||||
"use_adaptive_sampling": True, # 是否按水体宽度自适应
|
||||
"enabled": True,
|
||||
"output_path": "D:/sampling_spectra.csv", # 输出采样点 CSV
|
||||
"work_dir": "D:/workspace", # 工作目录（main_view 注入）
|
||||
})
|
||||
|
||||
返回字典字段：
|
||||
|
||||
* ``status`` : "completed" | "skipped" | "error"
|
||||
* ``output_path`` : 生成的 .csv 采样点光谱文件路径（失败时为 None）
|
||||
* ``message`` : 人类可读说明
|
||||
* ``mode`` : "adaptive" | "fixed"（采样模式，便于 UI 提示）
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict
|
||||
|
||||
from src.core.steps.prediction_step import PredictionStep
|
||||
|
||||
|
||||
def _resolve_output_dir(output_path: str | None, work_dir: str) -> Path:
|
||||
"""根据 output_path / work_dir 计算采样点输出目录"""
|
||||
if output_path:
|
||||
return Path(output_path).parent
|
||||
return Path(work_dir) / "4_Sampling"
|
||||
|
||||
|
||||
def execute_step4(config: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Step 4 后端计算入口——纯函数
|
||||
|
||||
Args:
|
||||
config: 由前端 view.get_config() 序列化、再经 main_view 注入 work_dir 的字典
|
||||
|
||||
Returns:
|
||||
标准结果字典 ``{status, output_path, message, mode}``
|
||||
"""
|
||||
# ---------- 入参规整 ----------
|
||||
deglint_img_path = config.get("deglint_img_path")
|
||||
water_mask_path = config.get("water_mask_path")
|
||||
interval = int(config.get("interval", 50))
|
||||
sample_radius = int(config.get("sample_radius", 5))
|
||||
chunk_size = int(config.get("chunk_size", 1000))
|
||||
use_adaptive_sampling = bool(config.get("use_adaptive_sampling", True))
|
||||
enabled = bool(config.get("enabled", True))
|
||||
output_path = config.get("output_path")
|
||||
work_dir = config.get("work_dir") or "."
|
||||
|
||||
output_dir = _resolve_output_dir(output_path, work_dir)
|
||||
mode = "adaptive" if use_adaptive_sampling else "fixed"
|
||||
|
||||
# ---------- 提前失败检查 ----------
|
||||
if not enabled:
|
||||
return {
|
||||
"status": "skipped",
|
||||
"output_path": None,
|
||||
"message": "用户禁用此步骤（enabled=False）",
|
||||
"mode": mode,
|
||||
}
|
||||
if not deglint_img_path:
|
||||
return {
|
||||
"status": "error",
|
||||
"output_path": None,
|
||||
"message": "未提供去耀斑影像路径（deglint_img_path）",
|
||||
"mode": mode,
|
||||
}
|
||||
if not Path(deglint_img_path).exists():
|
||||
return {
|
||||
"status": "error",
|
||||
"output_path": None,
|
||||
"message": f"去耀斑影像不存在: {deglint_img_path}",
|
||||
"mode": mode,
|
||||
}
|
||||
|
||||
# ---------- 执行（包一层 try/except 把异常转 dict，避免炸线程） ----------
|
||||
try:
|
||||
result_path = PredictionStep.generate_sampling_points(
|
||||
deglint_img_path=deglint_img_path,
|
||||
interval=interval,
|
||||
sample_radius=sample_radius,
|
||||
chunk_size=chunk_size,
|
||||
water_mask_path=water_mask_path,
|
||||
glint_mask_path=None, # 耀斑掩膜由 step2 产出，新架构未在 view 暴露
|
||||
output_dir=output_dir,
|
||||
use_adaptive_sampling=use_adaptive_sampling,
|
||||
callback=None, # 日志由 main_view 统一接管
|
||||
)
|
||||
except FileNotFoundError as e:
|
||||
return {
|
||||
"status": "error",
|
||||
"output_path": None,
|
||||
"message": f"文件不存在: {e}",
|
||||
"mode": mode,
|
||||
}
|
||||
except ValueError as e:
|
||||
return {
|
||||
"status": "error",
|
||||
"output_path": None,
|
||||
"message": f"参数错误: {e}",
|
||||
"mode": mode,
|
||||
}
|
||||
except Exception as e: # noqa: BLE001 —— service 层兜底捕获所有
|
||||
return {
|
||||
"status": "error",
|
||||
"output_path": None,
|
||||
"message": f"{type(e).__name__}: {e}",
|
||||
"mode": mode,
|
||||
}
|
||||
|
||||
# ---------- 成功路径 ----------
|
||||
p = Path(result_path)
|
||||
if not p.exists():
|
||||
return {
|
||||
"status": "error",
|
||||
"output_path": None,
|
||||
"message": f"PredictionStep.generate_sampling_points 未生成文件: {result_path}",
|
||||
"mode": mode,
|
||||
}
|
||||
|
||||
return {
|
||||
"status": "completed",
|
||||
"output_path": str(p).replace("\\", "/"),
|
||||
"message": f"采样点光谱数据已保存: {p.name}",
|
||||
"mode": mode,
|
||||
}
|
||||
136
src/new/services/step5_service.py
Normal file
136
src/new/services/step5_service.py
Normal file
@ -0,0 +1,136 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Step5 后端计算服务（数据清洗）
|
||||
====================================
|
||||
|
||||
纯计算函数——绝对不引用 PyQt、绝对不引用 main_view、绝对不读写全局变量。它只：
|
||||
|
||||
1. 从 ``config`` 字典读取参数；
|
||||
2. 调用旧版 ``DataPreparationStep.process_csv`` 读取水质 CSV，
|
||||
筛选剔除异常值后写出新的 CSV；
|
||||
3. 返回结果字典 ``{status, output_path, message, mode}``。
|
||||
|
||||
调用入口（由 main_view 在后台 QThread 中调用）：
|
||||
|
||||
execute_step5({
|
||||
"csv_path": "D:/water_quality.csv", # 输入水质参数 CSV
|
||||
"enabled": True,
|
||||
"output_path": "D:/processed_data.csv", # 处理后输出 CSV
|
||||
"work_dir": "D:/workspace", # 工作目录（main_view 注入）
|
||||
})
|
||||
|
||||
返回字典字段：
|
||||
|
||||
* ``status`` : "completed" | "skipped" | "error"
|
||||
* ``output_path`` : 生成的 .csv 清洗后文件路径（失败时为 None）
|
||||
* ``message`` : 人类可读说明
|
||||
* ``mode`` : "csv_clean"（便于 UI 提示）
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict
|
||||
|
||||
from src.core.steps.data_preparation_step import DataPreparationStep
|
||||
|
||||
|
||||
def _resolve_output_dir(output_path: str | None, work_dir: str) -> Path:
|
||||
"""根据 output_path / work_dir 计算清洗后 CSV 输出目录"""
|
||||
if output_path:
|
||||
return Path(output_path).parent
|
||||
return Path(work_dir) / "5_Data_Cleaning"
|
||||
|
||||
|
||||
def execute_step5(config: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Step 5 后端计算入口——纯函数
|
||||
|
||||
Args:
|
||||
config: 由前端 view.get_config() 序列化、再经 main_view 注入 work_dir 的字典
|
||||
|
||||
Returns:
|
||||
标准结果字典 ``{status, output_path, message, mode}``
|
||||
"""
|
||||
# ---------- 入参规整 ----------
|
||||
csv_path = config.get("csv_path")
|
||||
enabled = bool(config.get("enabled", True))
|
||||
output_path = config.get("output_path")
|
||||
work_dir = config.get("work_dir") or "."
|
||||
|
||||
output_dir = _resolve_output_dir(output_path, work_dir)
|
||||
mode = "csv_clean"
|
||||
|
||||
# ---------- 提前失败检查 ----------
|
||||
if not enabled:
|
||||
return {
|
||||
"status": "skipped",
|
||||
"output_path": None,
|
||||
"message": "用户禁用此步骤（enabled=False）",
|
||||
"mode": mode,
|
||||
}
|
||||
if not csv_path:
|
||||
return {
|
||||
"status": "error",
|
||||
"output_path": None,
|
||||
"message": "未提供水质参数 CSV 路径（csv_path）",
|
||||
"mode": mode,
|
||||
}
|
||||
if not Path(csv_path).exists():
|
||||
return {
|
||||
"status": "error",
|
||||
"output_path": None,
|
||||
"message": f"水质参数 CSV 不存在: {csv_path}",
|
||||
"mode": mode,
|
||||
}
|
||||
|
||||
# ---------- 显式构造 output_path（如未指定）----------
|
||||
# process_csv 内部会用 output_dir/processed_data.csv 自行拼接，
|
||||
# 此处显式指定 output_path 以保持契约一致。
|
||||
if not output_path:
|
||||
output_path = str(output_dir / "processed_data.csv").replace("\\", "/")
|
||||
|
||||
# ---------- 执行（包一层 try/except 把异常转 dict，避免炸线程） ----------
|
||||
try:
|
||||
result_path = DataPreparationStep.process_csv(
|
||||
csv_path=csv_path,
|
||||
output_dir=output_dir,
|
||||
callback=None, # 日志由 main_view 统一接管
|
||||
)
|
||||
except FileNotFoundError as e:
|
||||
return {
|
||||
"status": "error",
|
||||
"output_path": None,
|
||||
"message": f"文件不存在: {e}",
|
||||
"mode": mode,
|
||||
}
|
||||
except ValueError as e:
|
||||
return {
|
||||
"status": "error",
|
||||
"output_path": None,
|
||||
"message": f"参数错误: {e}",
|
||||
"mode": mode,
|
||||
}
|
||||
except Exception as e: # noqa: BLE001 —— service 层兜底捕获所有
|
||||
return {
|
||||
"status": "error",
|
||||
"output_path": None,
|
||||
"message": f"{type(e).__name__}: {e}",
|
||||
"mode": mode,
|
||||
}
|
||||
|
||||
# ---------- 成功路径 ----------
|
||||
p = Path(result_path)
|
||||
if not p.exists():
|
||||
return {
|
||||
"status": "error",
|
||||
"output_path": None,
|
||||
"message": f"DataPreparationStep.process_csv 未生成文件: {result_path}",
|
||||
"mode": mode,
|
||||
}
|
||||
|
||||
return {
|
||||
"status": "completed",
|
||||
"output_path": str(p).replace("\\", "/"),
|
||||
"message": f"清洗后 CSV 已保存: {p.name}",
|
||||
"mode": mode,
|
||||
}
|
||||
Reference in New Issue
Block a user