services/step10-13：终极决战！打通空间插值、可视化出图与报告生成的最后四步独立服务
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src/new/services/step12_service.py
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src/new/services/step12_service.py
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# -*- coding: utf-8 -*-
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"""
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Step12 后端计算服务（数据可视化——散点/光谱/箱线/掩膜缩略/采样地图）
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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。它只：
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1. 从 ``config`` 字典读取 ``generate_*`` 5 个开关；
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2. 按开关依次调用 ``src/core/visualization`` 或
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``src/postprocessing.visualization_reports`` 中的独立生成函数；
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3. 每个子任务独立 try/except 隔离，单个失败不影响其它；
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4. 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_step12({
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"work_dir": "D:/workspace", # 工作目录（必填）
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"img_dir": "D:/workspace/9_ML_Prediction", # 图像目录（可省，自动推断）
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"generate_scatter": True, # 模型评估散点图
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"generate_spectrum": True, # 光谱曲线图
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"generate_boxplots": True, # 箱线图
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"generate_glint_previews": True, # 掩膜/耀斑缩略图
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"generate_sampling_maps": True, # 采样点地图
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"models_dir": "D:/8_Supervised_Model_Training", # 散点图依赖（可省，自动推断）
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"training_csv_path": "D:/7_Water_Quality_Indices/training_with_indices.csv", # 光谱/箱线依赖（可省，自动推断）
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"output_dir": "D:/14_visualization", # 输出目录（可省 → work_dir/14_visualization）
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"enabled": True,
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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`` : 输出目录路径（失败时为 None）
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* ``message`` : 人类可读说明
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* ``mode`` : "viz_generate"（便于 UI 提示）
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设计取舍
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--------
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- 旧 panel 把所有图嵌入 matplotlib 画布；service 端只生成 PNG 文件，
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完全离线——view 层不再嵌入任何 chart widget。
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- 单个可视化失败时 ``results[sub] = {"status": "error", ...}``，
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但外层 status 仍然 = "completed"（局部失败不等于全部失败）。
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- 完全没有可用数据（连 work_dir 都不存在）时才返回 status="error"。
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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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def _resolve_output_dir(output_dir: str | None, work_dir: str) -> Path:
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if output_dir:
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return Path(output_dir)
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return Path(work_dir) / "14_visualization"
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def _resolve_models_dir(work_dir: str, models_dir: str | None) -> str:
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"""自动推断模型目录"""
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if models_dir and Path(models_dir).is_dir():
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return models_dir
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cand = Path(work_dir) / "8_Supervised_Model_Training"
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return str(cand) if cand.is_dir() else (models_dir or "")
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def _resolve_training_csv(work_dir: str, training_csv_path: str | None) -> str:
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"""自动推断训练 CSV"""
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if training_csv_path and Path(training_csv_path).is_file():
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return training_csv_path
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for sub in (
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"7_Water_Quality_Indices",
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"6_Spectral_Feature_Extraction",
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"4_processed_data",
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"visualization",
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):
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d = Path(work_dir) / sub
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if not d.is_dir():
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continue
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cands = sorted(d.glob("*.csv"))
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if cands:
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return str(cands[0])
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return training_csv_path or ""
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def _try_scatter(work_dir: str, output_dir: Path) -> Dict[str, Any]:
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"""生成模型评估散点图（依赖 models_dir + training_csv_path）"""
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from src.core.visualization.scatter_plot import generate_model_scatter_plots
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models_dir = _resolve_models_dir(work_dir, None)
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training_csv = _resolve_training_csv(work_dir, None)
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if not models_dir or not Path(models_dir).is_dir():
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raise FileNotFoundError(f"模型目录不存在: {models_dir or '(自动推断失败)'}")
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if not training_csv or not Path(training_csv).is_file():
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raise FileNotFoundError(f"训练 CSV 不存在: {training_csv or '(自动推断失败)'}")
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out_sub = output_dir / "scatter_plots"
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paths = generate_model_scatter_plots(
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models_dir=models_dir,
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training_csv_path=training_csv,
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output_dir=str(out_sub),
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)
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return {"status": "completed", "count": len(paths), "output_dir": str(out_sub)}
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def _try_spectrum(work_dir: str, output_dir: Path) -> Dict[str, Any]:
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"""生成光谱曲线对比图（依赖 training CSV）"""
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from src.core.visualization.spectrum_plot import generate_spectrum_comparison_plots
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training_csv = _resolve_training_csv(work_dir, None)
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if not training_csv or not Path(training_csv).is_file():
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raise FileNotFoundError(f"训练 CSV 不存在: {training_csv or '(自动推断失败)'}")
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out_sub = output_dir / "spectrum_plots"
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paths = generate_spectrum_comparison_plots(
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csv_path=training_csv,
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output_dir=str(out_sub),
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)
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return {"status": "completed", "count": len(paths), "output_dir": str(out_sub)}
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def _try_boxplots(work_dir: str, output_dir: Path) -> Dict[str, Any]:
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"""生成水质参数箱型图（依赖 training CSV）"""
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from src.core.visualization.boxplot import generate_boxplots
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training_csv = _resolve_training_csv(work_dir, None)
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if not training_csv or not Path(training_csv).is_file():
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raise FileNotFoundError(f"训练 CSV 不存在: {training_csv or '(自动推断失败)'}")
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out_sub = output_dir / "boxplots"
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paths = generate_boxplots(
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csv_path=training_csv,
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output_dir=str(out_sub),
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)
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return {"status": "completed", "count": len(paths), "output_dir": str(out_sub)}
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def _try_glint_previews(work_dir: str, output_dir: Path) -> Dict[str, Any]:
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"""生成耀斑分析影像预览图"""
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from src.postprocessing.visualization_reports import ReportGenerator
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rg = ReportGenerator(output_dir=str(output_dir))
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paths = rg.generate_glint_deglint_previews(
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work_dir=work_dir,
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output_subdir="glint_deglint_previews",
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generate_glint=True,
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generate_deglint=True,
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)
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return {"status": "completed", "count": len(paths), "output_dir": str(output_dir / "glint_deglint_previews")}
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def _try_sampling_maps(work_dir: str, output_dir: Path) -> Dict[str, Any]:
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"""生成采样点地图"""
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from src.postprocessing.visualization_reports import ReportGenerator
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rg = ReportGenerator(output_dir=str(output_dir))
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p = rg.generate_sampling_point_map(output_subdir="sampling_maps")
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return {"status": "completed" if p else "error", "path": p, "output_dir": str(output_dir / "sampling_maps")}
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def execute_step12(config: Dict[str, Any]) -> Dict[str, Any]:
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"""Step 12 后端计算入口——纯函数"""
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work_dir: str = config.get("work_dir") or ""
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img_dir: str = config.get("img_dir") or ""
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enabled: bool = bool(config.get("enabled", True))
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output_dir: str = config.get("output_dir") or ""
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# 5 个开关：缺省默认 True（与旧 panel 行为一致）
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gen_scatter = bool(config.get("generate_scatter", True))
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gen_spectrum = bool(config.get("generate_spectrum", True))
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gen_boxplots = bool(config.get("generate_boxplots", True))
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gen_glint = bool(config.get("generate_glint_previews", True))
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gen_sampling = bool(config.get("generate_sampling_maps", True))
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output_path = _resolve_output_dir(output_dir, work_dir)
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mode = "viz_generate"
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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": mode,
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}
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if not work_dir:
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return {
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"status": "error",
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"output_path": None,
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"message": "未提供工作目录（work_dir）",
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"mode": mode,
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}
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if not Path(work_dir).is_dir():
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return {
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"status": "error",
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"output_path": None,
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"message": f"工作目录不存在: {work_dir}",
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"mode": mode,
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}
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output_path.mkdir(parents=True, exist_ok=True)
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# img_dir 仅作提示用，不强制校验
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_ = img_dir
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# ---------- 逐项执行（独立 try/except 隔离） ----------
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results: Dict[str, Dict[str, Any]] = {}
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n_ok = 0
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n_skip = 0
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n_err = 0
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tasks = []
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if gen_scatter:
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tasks.append(("scatter", _try_scatter))
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if gen_spectrum:
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tasks.append(("spectrum", _try_spectrum))
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if gen_boxplots:
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tasks.append(("boxplots", _try_boxplots))
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if gen_glint:
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tasks.append(("glint_previews", _try_glint_previews))
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if gen_sampling:
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tasks.append(("sampling_maps", _try_sampling_maps))
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if not tasks:
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return {
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"status": "completed",
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"output_path": str(output_path).replace("\\", "/"),
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"message": "无可视化任务（5 个开关全部 False）",
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"mode": mode,
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}
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print(f"[Step12 Service] 工作目录: {work_dir}")
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print(f"[Step12 Service] 输出目录: {output_path}")
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print(f"[Step12 Service] 子任务: {[n for n, _ in tasks]}")
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for name, fn in tasks:
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try:
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r = fn(work_dir, output_path)
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results[name] = r
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if r.get("status") == "completed":
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n_ok += 1
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print(f" ✅ {name}: count={r.get('count', r.get('path', '?'))}")
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else:
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n_err += 1
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print(f" ⚠ {name}: status={r.get('status')}")
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except FileNotFoundError as e:
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results[name] = {"status": "skipped", "reason": str(e)}
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n_skip += 1
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print(f" ↻ {name}: 跳过（缺数据）: {e}")
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except Exception as e: # noqa: BLE001
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results[name] = {"status": "error", "message": f"{type(e).__name__}: {e}"}
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n_err += 1
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print(f" ❌ {name}: {type(e).__name__}: {e}")
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# ---------- 汇总 ----------
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# 局部失败不算全局失败——只有当所有任务都缺数据时才算 skipped
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if n_ok == 0 and n_err == 0:
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return {
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"status": "skipped",
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"output_path": None,
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"message": f"全部 {n_skip} 个子任务缺数据被跳过（models_dir/training_csv 等前置产物不存在）",
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"mode": mode,
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}
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return {
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"status": "completed",
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"output_path": str(output_path).replace("\\", "/"),
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"message": (
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f"可视化完成：成功 {n_ok} / 失败 {n_err} / 跳过 {n_skip}；"
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f"目录: {output_path.name or output_path}"
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),
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"mode": mode,
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}
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