From 67aaaaa6b2810339298dfef003e4970b16eaaf48 Mon Sep 17 00:00:00 2001 From: DXC Date: Wed, 24 Jun 2026 12:52:56 +0800 Subject: [PATCH] =?UTF-8?q?refactor(step10):=20=E6=8B=86=E5=88=86=20WaterI?= =?UTF-8?q?ndexCsvProcessor=20=E5=88=B0=E7=8B=AC=E7=AB=8B=E5=AD=90?= =?UTF-8?q?=E6=A8=A1=E5=9D=97=20+=20smoke=20test?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- _smoke_test_step10.py | 180 ++++++++++++++ .../waterindex_inversion/__init__.py | 183 +-------------- .../waterindex_inversion/csv_processor.py | 220 ++++++++++++++++++ 3 files changed, 405 insertions(+), 178 deletions(-) create mode 100644 _smoke_test_step10.py create mode 100644 src/core/algorithms/waterindex_inversion/csv_processor.py diff --git a/_smoke_test_step10.py b/_smoke_test_step10.py new file mode 100644 index 0000000..78c54f8 --- /dev/null +++ b/_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, + 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) diff --git a/src/core/algorithms/waterindex_inversion/__init__.py b/src/core/algorithms/waterindex_inversion/__init__.py index 0d2723e..fb6bccc 100644 --- a/src/core/algorithms/waterindex_inversion/__init__.py +++ b/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） # ------------------------------------------------------------------ - -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, ``， - 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, - 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 +# WaterIndexCsvProcessor 已拆出到独立子模块 csv_processor.py， +# 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` +# 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 # 保留旧 import 路径兼容 diff --git a/src/core/algorithms/waterindex_inversion/csv_processor.py b/src/core/algorithms/waterindex_inversion/csv_processor.py new file mode 100644 index 0000000..58374be --- /dev/null +++ b/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, ``。 +""" + +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, ``， + 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, + 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 \ No newline at end of file