refactor(step10): 拆分 WaterIndexCsvProcessor 到独立子模块 + smoke test

_smoke_test_step10.py

@@ -0,0 +1,180 @@
+"""
+Smoke test for Step 10 散点 CSV 模式 (WaterIndexCsvProcessor)
+
+模拟 Step 4 输出格式 (sampling_spectra.csv):
+    x_coord, y_coord, pixel_x, pixel_y, "400.000000", "401.000000", ...
+
+验证 WaterIndexCsvProcessor.compute_indices_from_csv:
+  1. 正确读取 x_coord/y_coord → 重命名为 longitude/latitude
+  2. 正确识别数字列名 = 光谱列
+  3. 复用 WaterQualityIndexCalculator 逐行计算
+  4. 输出每个公式一个 CSV,三列严格为 longitude, latitude, <formula_name>
+  5. 公式值数量级合理 (非 NaN,非 inf)
+"""
+import os
+import sys
+import tempfile
+import shutil
+from pathlib import Path
+
+# 让脚本能找到项目根
+PROJECT_ROOT = Path(__file__).parent
+sys.path.insert(0, str(PROJECT_ROOT))
+
+
+def create_synthetic_sampling_csv(path: str, n_points: int = 5):
+    """模拟 Step 4 输出: x_coord, y_coord, pixel_x, pixel_y, 数字列名光谱"""
+    import csv
+    # 选一组关键波段（确保 waterindex.csv 中的 BGA_Am09KBBI 等公式都能找到）
+    wavelengths = [400.0, 443.0, 458.0, 486.0, 500.0, 510.0, 531.0, 547.0, 555.0,
+                   615.0, 622.0, 629.0, 644.0, 658.0, 665.0, 672.0, 681.0, 686.0,
+                   700.0, 709.0, 714.0, 715.0, 753.0, 857.0, 900.0]
+    fieldnames = ['x_coord', 'y_coord', 'pixel_x', 'pixel_y'] + [f'{w:.6f}' for w in wavelengths]
+
+    with open(path, 'w', newline='', encoding='utf-8-sig') as f:
+        writer = csv.DictWriter(f, fieldnames=fieldnames)
+        writer.writeheader()
+        for i in range(n_points):
+            # 模拟水体光谱（典型内陆湖泊反射率 0.005-0.05）
+            row = {
+                'x_coord': 100.0 + i * 10,
+                'y_coord': 30.0 + i * 5,
+                'pixel_x': 100 + i,
+                'pixel_y': 30 + i,
+            }
+            for w in wavelengths:
+                # 简单合成光谱: 蓝光 < 红光 + 一点叶绿素峰
+                base = 0.01 + 0.0001 * (w - 400)
+                chl_peak = 0.005 * (1 - abs(w - 560) / 200) if abs(w - 560) < 200 else 0
+                row[f'{w:.6f}'] = round(base + chl_peak, 6)
+            writer.writerow(row)
+
+
+def run_smoke():
+    print("=" * 70)
+    print("Step 10 散点 CSV 模式 Smoke Test")
+    print("=" * 70)
+
+    tmpdir = tempfile.mkdtemp(prefix="step10_smoke_")
+    print(f"Tempdir: {tmpdir}")
+
+    sampling_csv = os.path.join(tmpdir, "sampling_spectra.csv")
+    output_dir = os.path.join(tmpdir, "10_WaterIndex_CSV")
+    os.makedirs(output_dir, exist_ok=True)
+
+    # 1) 创建合成 sampling CSV
+    create_synthetic_sampling_csv(sampling_csv, n_points=5)
+    print(f"Created sampling CSV: {sampling_csv}")
+    with open(sampling_csv, encoding='utf-8-sig') as f:
+        header_line = f.readline().strip()
+    print(f"  header: {header_line[:120]}...")
+
+    # 2) 找到项目自带的 waterindex.csv
+    waterindex_csv = PROJECT_ROOT / "src" / "gui" / "model" / "waterindex.csv"
+    print(f"Using waterindex.csv: {waterindex_csv}")
+    assert waterindex_csv.is_file(), "waterindex.csv not found!"
+
+    # 3) 调 WaterIndexCsvProcessor
+    # 注意：父包 __init__.py 顶部有 `from osgeo import gdal, osr`，
+    #       在没装 gdal 的 venv 里任何 from ...waterindex_inversion import ... 都会炸。
+    #       这里用 importlib 按文件路径直接加载 csv_processor.py 子模块，
+    #       完全绕开 __init__.py 的 osgeo 加载。
+    import importlib.util as _ilu
+    _csv_proc_path = (
+        PROJECT_ROOT / "src" / "core" / "algorithms" / "waterindex_inversion"
+        / "csv_processor.py"
+    )
+    _spec = _ilu.spec_from_file_location("waterindex_csv_processor", _csv_proc_path)
+    _mod = _ilu.module_from_spec(_spec)
+    sys.modules["waterindex_csv_processor"] = _mod
+    _spec.loader.exec_module(_mod)
+    WaterIndexCsvProcessor = _mod.WaterIndexCsvProcessor
+
+    progress_log = []
+    def progress_cb(msg, pct):
+        progress_log.append((msg, pct))
+
+    proc = WaterIndexCsvProcessor(str(waterindex_csv))
+    print(f"\n[Step] compute_indices_from_csv...")
+    out_files = proc.compute_indices_from_csv(
+        sampling_csv_path=sampling_csv,
+        output_dir=output_dir,
+        selected_formulas=["BGA_Am09KBBI", "BGA_Da052BDA", "BGA_Be16NDPhyI"],
+        progress_callback=progress_cb,
+    )
+
+    print(f"\n[Result] Generated {len(out_files)} CSV files:")
+    for name, path in out_files.items():
+        size = os.path.getsize(path)
+        print(f"  {name:30s} -> {os.path.basename(path)}  ({size} bytes)")
+
+    # 4) 验证每个输出 CSV 的列结构
+    print(f"\n[Verify] Column structure check:")
+    all_pass = True
+    import pandas as pd
+    for name, path in out_files.items():
+        df = pd.read_csv(path, encoding='utf-8-sig')
+        cols = list(df.columns)
+        expected = ['longitude', 'latitude', name]
+        ok = (cols == expected) and (len(df) == 5)
+        flag = "✓" if ok else "✗"
+        if not ok:
+            all_pass = False
+        print(f"  {flag} {name:30s}  cols={cols}  rows={len(df)}")
+
+    # 5) 验证坐标重命名
+    print(f"\n[Verify] Coordinate rename (x_coord→longitude, y_coord→latitude):")
+    sample = pd.read_csv(out_files[list(out_files.keys())[0]], encoding='utf-8-sig')
+    print(f"  longitude values: {sample['longitude'].tolist()}")
+    print(f"  latitude  values: {sample['latitude'].tolist()}")
+    coord_ok = (sample['longitude'].iloc[0] == 100.0 and
+                sample['latitude'].iloc[0] == 30.0)
+    if not coord_ok:
+        all_pass = False
+    print(f"  {'✓' if coord_ok else '✗'} coordinate rename correct")
+
+    # 6) 验证公式值非 NaN
+    print(f"\n[Verify] Formula values (no NaN):")
+    for name, path in out_files.items():
+        df = pd.read_csv(path, encoding='utf-8-sig')
+        col = df[name]
+        n_nan = col.isna().sum()
+        n_inf = ((col == float('inf')) | (col == float('-inf'))).sum()
+        all_nan = col.dropna().empty
+        if n_nan > 0 or n_inf > 0 or all_nan:
+            print(f"  ✗ {name:30s}: NaN={n_nan} Inf={n_inf} empty={all_nan}")
+            print(f"     values: {col.tolist()}")
+            all_pass = False
+        else:
+            mn, mx = col.min(), col.max()
+            print(f"  ✓ {name:30s}: range=[{mn:.4f}, {mx:.4f}]")
+
+    # 7) 进度回调检查
+    print(f"\n[Verify] Progress callback:")
+    print(f"  Total progress events: {len(progress_log)}")
+    if progress_log:
+        first_msg, first_pct = progress_log[0]
+        last_msg, last_pct = progress_log[-1]
+        print(f"  First: ({first_pct:.1f}%) {first_msg}")
+        print(f"  Last : ({last_pct:.1f}%) {last_msg}")
+        progress_ok = (last_pct == 100.0)
+        if not progress_ok:
+            all_pass = False
+        print(f"  {'✓' if progress_ok else '✗'} last progress = 100%")
+
+    # 8) 总结
+    print(f"\n{'=' * 70}")
+    if all_pass:
+        print(f"✓ ALL CHECKS PASSED")
+    else:
+        print(f"✗ SOME CHECKS FAILED — inspect output above")
+    print(f"{'=' * 70}")
+
+    # 清理
+    shutil.rmtree(tmpdir, ignore_errors=True)
+    return all_pass
+
+
+if __name__ == "__main__":
+    success = run_smoke()
+    sys.exit(0 if success else 1)

src/core/algorithms/waterindex_inversion/__init__.py

@@ -649,184 +649,11 @@ class WaterIndexProcessor:
 # ------------------------------------------------------------------
 # 散点处理入口（Step 10 重构后使用，与 Step 9 对称）
 # ------------------------------------------------------------------
-
+# WaterIndexCsvProcessor 已拆出到独立子模块 csv_processor.py，
-class WaterIndexCsvProcessor:
+# 目的是让纯 CSV 计算链路不再被 __init__.py 顶部 osgeo import 拖垮。
-    """
+# 这里做一次 re-export，保留所有 `from src.core.algorithms.waterindex_inversion import WaterIndexCsvProcessor`
-    散点 CSV 驱动的水色指数反演器。
+# 这类已有 import 路径仍能正常工作（生产环境/打包后）。
-
+from src.core.algorithms.waterindex_inversion.csv_processor import WaterIndexCsvProcessor  # noqa: E402,F401
-    设计目的
-    --------
-    与 Step 9 (ML 预测) 完全对称的【散点处理模式】：
-
-    * 输入：Step 4 生成的 ``sampling_spectra.csv``，列结构为
-      ``x_coord, y_coord, pixel_x, pixel_y, 400.000000, 401.000000, ...``
-    * 处理：解析 ``waterindex.csv`` 中的公式，对每行采样点
-            提取对应波段数值、逐行 eval 计算水色指数
-    * 输出：每个公式一个 CSV，列严格为 ``longitude, latitude, <formula_name>``，
-            可直接喂给 Step 11 ContentMapper
-
-    输出目录
-    --------
-    默认 ``{work_dir}/10_WaterIndex_CSV/``；若用户指定 ``output_dir`` 则用其值。
-    """
-
-    COORD_RENAME_MAP = {
-        "x_coord": "longitude",
-        "y_coord": "latitude",
-        "lon": "longitude",
-        "lat": "latitude",
-    }
-
-    def __init__(self, waterindex_csv_path: Optional[str] = None):
-        if waterindex_csv_path is None:
-            candidates = [
-                os.path.join(os.path.dirname(__file__), '..', '..', 'gui', 'model', 'waterindex.csv'),
-                os.path.join(os.path.dirname(__file__), '..', '..', '..', 'gui', 'model', 'waterindex.csv'),
-            ]
-            for p in candidates:
-                if os.path.isfile(p):
-                    waterindex_csv_path = p
-                    break
-        self.waterindex_csv_path = waterindex_csv_path
-        self._index_calc = None
-
-    def _get_index_calc(self):
-        """懒加载 WaterQualityIndexCalculator（首次访问时实例化）"""
-        if self._index_calc is None and self.waterindex_csv_path:
-            from src.utils.water_index import WaterQualityIndexCalculator
-            self._index_calc = WaterQualityIndexCalculator(self.waterindex_csv_path)
-        return self._index_calc
-
-    @staticmethod
-    def _detect_wavelength_columns(df: "pd.DataFrame") -> List[str]:
-        """识别光谱列：列名是浮点数字符串（Step 4 输出的 '400.000000' 形式）"""
-        import re
-        wl_cols = []
-        for col in df.columns:
-            try:
-                float(str(col).strip())
-                wl_cols.append(col)
-            except (ValueError, TypeError):
-                continue
-        return wl_cols
-
-    @staticmethod
-    def _safe_filename(name: str) -> str:
-        """公式名 → 文件名安全字符（与旧 BSQ 输出命名习惯一致）"""
-        return re.sub(r'[^\w\u4e00-\u9fff-]', '_', name).strip('_') or 'index'
-
-    def compute_indices_from_csv(
-        self,
-        sampling_csv_path: str,
-        output_dir: str,
-        selected_formulas: Optional[List[str]] = None,
-        progress_callback: Optional[Callable[[str, float], None]] = None,
-    ) -> Dict[str, str]:
-        """
-        散点 CSV → 按指数拆分的多个 CSV。
-
-        Parameters
-        ----------
-        sampling_csv_path : str
-            Step 4 输出的 ``sampling_spectra.csv`` 路径
-        output_dir : str
-            输出目录；不存在会自动创建
-        selected_formulas : list, optional
-            要计算的公式名列表；None 或空列表 = 全部公式
-        progress_callback : callable, optional
-            进度回调 ``(msg: str, pct: float)``
-
-        Returns
-        -------
-        dict
-            ``{公式名: 输出 CSV 路径}``
-        """
-        def notify(msg: str, pct: float) -> None:
-            if progress_callback:
-                progress_callback(msg, pct)
-
-        if not os.path.isfile(sampling_csv_path):
-            raise FileNotFoundError(f"采样点 CSV 不存在: {sampling_csv_path}")
-
-        if not self.waterindex_csv_path or not os.path.isfile(self.waterindex_csv_path):
-            raise FileNotFoundError(
-                f"waterindex.csv 未配置或不存在: {self.waterindex_csv_path}"
-            )
-
-        os.makedirs(output_dir, exist_ok=True)
-
-        notify("正在读取采样点 CSV…", 5)
-        import pandas as pd
-        df = pd.read_csv(sampling_csv_path, encoding="utf-8-sig")
-        if df.empty:
-            raise ValueError(f"采样点 CSV 为空: {sampling_csv_path}")
-
-        # 坐标列重命名（x_coord → longitude, y_coord → latitude）
-        df = df.rename(columns={k: v for k, v in self.COORD_RENAME_MAP.items()
-                                if k in df.columns})
-        if "longitude" not in df.columns or "latitude" not in df.columns:
-            raise ValueError(
-                f"采样点 CSV 缺少坐标列（期望 x_coord/y_coord 或 longitude/latitude），"
-                f"实际列: {list(df.columns)}"
-            )
-
-        # 识别光谱列
-        wl_cols = self._detect_wavelength_columns(df)
-        if not wl_cols:
-            raise ValueError(
-                f"采样点 CSV 中未识别到任何光谱列（列名为数字）,"
-                f"实际列: {list(df.columns)}"
-            )
-        notify(f"识别到 {len(wl_cols)} 个光谱列, 采样点 {len(df)} 个", 15)
-
-        calc = self._get_index_calc()
-        if calc is None:
-            raise RuntimeError("WaterQualityIndexCalculator 初始化失败")
-
-        all_formula_names = calc.list_available()
-        if selected_formulas:
-            targets = [n for n in selected_formulas if n in all_formula_names]
-            missing = [n for n in selected_formulas if n not in all_formula_names]
-            if missing:
-                print(f"[WaterIndexCsvProcessor] 警告: 以下公式未在 waterindex.csv 中找到,已跳过: {missing}")
-        else:
-            targets = all_formula_names
-
-        if not targets:
-            raise ValueError("没有可计算的公式（selected_formulas 为空且 waterindex.csv 中无公式）")
-
-        # 一次性算出所有目标公式的 Series（避免重复遍历 DataFrame）
-        notify(f"开始逐行计算 {len(targets)} 个公式…", 25)
-        spectra_df = df[wl_cols]
-        try:
-            results_df = calc.calculate_many(targets, spectra_df)
-        except Exception as e:
-            raise RuntimeError(f"公式计算失败: {e}")
-
-        # 每个公式一个 CSV：longitude, latitude, <formula_name>
-        out_files: Dict[str, str] = {}
-        n_total = len(targets)
-        for i, name in enumerate(targets):
-            try:
-                per_idx = results_df[name]
-                out_df = pd.DataFrame({
-                    "longitude": df["longitude"].values,
-                    "latitude": df["latitude"].values,
-                    name: per_idx.values,
-                })
-                out_path = os.path.join(output_dir, f"{self._safe_filename(name)}.csv")
-                out_df.to_csv(out_path, index=False, float_format="%.6f", encoding="utf-8-sig")
-                out_files[name] = out_path
-                notify(
-                    f"[{i + 1}/{n_total}] {name} → {os.path.basename(out_path)}",
-                    25 + 70 * (i + 1) / n_total,
-                )
-            except Exception as e:
-                print(f"[WaterIndexCsvProcessor] 公式 '{name}' 失败: {e}")
-                continue
-
-        notify(f"完成！共输出 {len(out_files)} / {n_total} 个指数 CSV", 100)
-        return out_files
 
 
 # 保留旧 import 路径兼容

src/core/algorithms/waterindex_inversion/csv_processor.py

@@ -0,0 +1,220 @@
+# -*- coding: utf-8 -*-
+"""
+水色指数反演 — 散点 CSV 模式处理器（独立子模块）。
+
+设计意图
+--------
+本模块与 ``waterindex_inversion.__init__.py`` 中的 ``WaterIndexProcessor``
+(栅格 BSQ 模式) **彻底解耦**，不依赖任何 osgeo / rasterio / gdal，仅依赖
+``pandas`` 与 ``src.utils.water_index.WaterQualityIndexCalculator``。
+
+**为什么独立成文件？**
+
+``__init__.py`` 顶部有 ``from osgeo import gdal, osr``（用于 BSQ 栅格模式），
+这意味着任何 ``from src.core.algorithms.waterindex_inversion import X``
+都会触发 osgeo 加载——而某些验证环境（无 gdal 包的 venv）会因此 ImportError。
+
+本子模块独立后，可通过
+``from src.core.algorithms.waterindex_inversion.csv_processor import WaterIndexCsvProcessor``
+直接加载，**完全不触发** ``__init__.py`` 的 osgeo import 链，便于无 gdal 环境
+做端到端 smoke test。
+
+**调用入口（由 Step 10 service / panel 调用）**::
+
+    from src.core.algorithms.waterindex_inversion.csv_processor import WaterIndexCsvProcessor
+    proc = WaterIndexCsvProcessor(waterindex_csv_path)
+    out = proc.compute_indices_from_csv(
+        sampling_csv_path=...,
+        output_dir=...,
+        selected_formulas=[...],
+        progress_callback=lambda msg, pct: ...,
+    )
+
+输出格式
+--------
+每个公式一个 CSV，三列严格为 ``longitude, latitude, <formula_name>``。
+"""
+
+from __future__ import annotations
+
+import os
+import re
+from typing import Callable, Dict, List, Optional
+
+
+class WaterIndexCsvProcessor:
+    """
+    散点 CSV 驱动的水色指数反演器。
+
+    设计目的
+    --------
+    与 Step 9 (ML 预测) 完全对称的【散点处理模式】：
+
+    * 输入：Step 4 生成的 ``sampling_spectra.csv``，列结构为
+      ``x_coord, y_coord, pixel_x, pixel_y, 400.000000, 401.000000, ...``
+    * 处理：解析 ``waterindex.csv`` 中的公式，对每行采样点
+            提取对应波段数值、逐行 eval 计算水色指数
+    * 输出：每个公式一个 CSV，列严格为 ``longitude, latitude, <formula_name>``，
+            可直接喂给 Step 11 ContentMapper
+
+    输出目录
+    --------
+    默认 ``{work_dir}/10_WaterIndex_CSV/``；若用户指定 ``output_dir`` 则用其值。
+    """
+
+    COORD_RENAME_MAP = {
+        "x_coord": "longitude",
+        "y_coord": "latitude",
+        "lon": "longitude",
+        "lat": "latitude",
+    }
+
+    def __init__(self, waterindex_csv_path: Optional[str] = None):
+        if waterindex_csv_path is None:
+            candidates = [
+                os.path.join(os.path.dirname(__file__), '..', '..', 'gui', 'model', 'waterindex.csv'),
+                os.path.join(os.path.dirname(__file__), '..', '..', '..', 'gui', 'model', 'waterindex.csv'),
+            ]
+            for p in candidates:
+                if os.path.isfile(p):
+                    waterindex_csv_path = p
+                    break
+        self.waterindex_csv_path = waterindex_csv_path
+        self._index_calc = None
+
+    def _get_index_calc(self):
+        """懒加载 WaterQualityIndexCalculator（首次访问时实例化）"""
+        if self._index_calc is None and self.waterindex_csv_path:
+            from src.utils.water_index import WaterQualityIndexCalculator
+            self._index_calc = WaterQualityIndexCalculator(self.waterindex_csv_path)
+        return self._index_calc
+
+    @staticmethod
+    def _detect_wavelength_columns(df: "object") -> List[str]:
+        """识别光谱列：列名是浮点数字符串（Step 4 输出的 '400.000000' 形式）"""
+        wl_cols = []
+        for col in df.columns:
+            try:
+                float(str(col).strip())
+                wl_cols.append(col)
+            except (ValueError, TypeError):
+                continue
+        return wl_cols
+
+    @staticmethod
+    def _safe_filename(name: str) -> str:
+        """公式名 → 文件名安全字符（与旧 BSQ 输出命名习惯一致）"""
+        return re.sub(r'[^\w\u4e00-\u9fff-]', '_', name).strip('_') or 'index'
+
+    def compute_indices_from_csv(
+        self,
+        sampling_csv_path: str,
+        output_dir: str,
+        selected_formulas: Optional[List[str]] = None,
+        progress_callback: Optional[Callable[[str, float], None]] = None,
+    ) -> Dict[str, str]:
+        """
+        散点 CSV → 按指数拆分的多个 CSV。
+
+        Parameters
+        ----------
+        sampling_csv_path : str
+            Step 4 输出的 ``sampling_spectra.csv`` 路径
+        output_dir : str
+            输出目录；不存在会自动创建
+        selected_formulas : list, optional
+            要计算的公式名列表；None 或空列表 = 全部公式
+        progress_callback : callable, optional
+            进度回调 ``(msg: str, pct: float)``
+
+        Returns
+        -------
+        dict
+            ``{公式名: 输出 CSV 路径}``
+        """
+        def notify(msg: str, pct: float) -> None:
+            if progress_callback:
+                progress_callback(msg, pct)
+
+        if not os.path.isfile(sampling_csv_path):
+            raise FileNotFoundError(f"采样点 CSV 不存在: {sampling_csv_path}")
+
+        if not self.waterindex_csv_path or not os.path.isfile(self.waterindex_csv_path):
+            raise FileNotFoundError(
+                f"waterindex.csv 未配置或不存在: {self.waterindex_csv_path}"
+            )
+
+        os.makedirs(output_dir, exist_ok=True)
+
+        notify("正在读取采样点 CSV…", 5)
+        import pandas as pd
+        df = pd.read_csv(sampling_csv_path, encoding="utf-8-sig")
+        if df.empty:
+            raise ValueError(f"采样点 CSV 为空: {sampling_csv_path}")
+
+        # 坐标列重命名（x_coord → longitude, y_coord → latitude）
+        df = df.rename(columns={k: v for k, v in self.COORD_RENAME_MAP.items()
+                                if k in df.columns})
+        if "longitude" not in df.columns or "latitude" not in df.columns:
+            raise ValueError(
+                f"采样点 CSV 缺少坐标列（期望 x_coord/y_coord 或 longitude/latitude），"
+                f"实际列: {list(df.columns)}"
+            )
+
+        # 识别光谱列
+        wl_cols = self._detect_wavelength_columns(df)
+        if not wl_cols:
+            raise ValueError(
+                f"采样点 CSV 中未识别到任何光谱列（列名为数字）,"
+                f"实际列: {list(df.columns)}"
+            )
+        notify(f"识别到 {len(wl_cols)} 个光谱列, 采样点 {len(df)} 个", 15)
+
+        calc = self._get_index_calc()
+        if calc is None:
+            raise RuntimeError("WaterQualityIndexCalculator 初始化失败")
+
+        all_formula_names = calc.list_available()
+        if selected_formulas:
+            targets = [n for n in selected_formulas if n in all_formula_names]
+            missing = [n for n in selected_formulas if n not in all_formula_names]
+            if missing:
+                print(f"[WaterIndexCsvProcessor] 警告: 以下公式未在 waterindex.csv 中找到,已跳过: {missing}")
+        else:
+            targets = all_formula_names
+
+        if not targets:
+            raise ValueError("没有可计算的公式（selected_formulas 为空且 waterindex.csv 中无公式）")
+
+        # 一次性算出所有目标公式的 Series（避免重复遍历 DataFrame）
+        notify(f"开始逐行计算 {len(targets)} 个公式…", 25)
+        spectra_df = df[wl_cols]
+        try:
+            results_df = calc.calculate_many(targets, spectra_df)
+        except Exception as e:
+            raise RuntimeError(f"公式计算失败: {e}")
+
+        # 每个公式一个 CSV：longitude, latitude, <formula_name>
+        out_files: Dict[str, str] = {}
+        n_total = len(targets)
+        for i, name in enumerate(targets):
+            try:
+                per_idx = results_df[name]
+                out_df = pd.DataFrame({
+                    "longitude": df["longitude"].values,
+                    "latitude": df["latitude"].values,
+                    name: per_idx.values,
+                })
+                out_path = os.path.join(output_dir, f"{self._safe_filename(name)}.csv")
+                out_df.to_csv(out_path, index=False, float_format="%.6f", encoding="utf-8-sig")
+                out_files[name] = out_path
+                notify(
+                    f"[{i + 1}/{n_total}] {name} → {os.path.basename(out_path)}",
+                    25 + 70 * (i + 1) / n_total,
+                )
+            except Exception as e:
+                print(f"[WaterIndexCsvProcessor] 公式 '{name}' 失败: {e}")
+                continue
+
+        notify(f"完成！共输出 {len(out_files)} / {n_total} 个指数 CSV", 100)
+        return out_files