refactor(step10): 拆分 WaterIndexCsvProcessor 到独立子模块 + smoke test
This commit is contained in:
180
_smoke_test_step10.py
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180
_smoke_test_step10.py
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"""
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Smoke test for Step 10 散点 CSV 模式 (WaterIndexCsvProcessor)
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模拟 Step 4 输出格式 (sampling_spectra.csv):
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x_coord, y_coord, pixel_x, pixel_y, "400.000000", "401.000000", ...
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验证 WaterIndexCsvProcessor.compute_indices_from_csv:
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1. 正确读取 x_coord/y_coord → 重命名为 longitude/latitude
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2. 正确识别数字列名 = 光谱列
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3. 复用 WaterQualityIndexCalculator 逐行计算
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4. 输出每个公式一个 CSV,三列严格为 longitude, latitude, <formula_name>
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5. 公式值数量级合理 (非 NaN,非 inf)
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"""
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import os
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import sys
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import tempfile
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import shutil
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from pathlib import Path
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# 让脚本能找到项目根
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PROJECT_ROOT = Path(__file__).parent
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sys.path.insert(0, str(PROJECT_ROOT))
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def create_synthetic_sampling_csv(path: str, n_points: int = 5):
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"""模拟 Step 4 输出: x_coord, y_coord, pixel_x, pixel_y, 数字列名光谱"""
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import csv
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# 选一组关键波段（确保 waterindex.csv 中的 BGA_Am09KBBI 等公式都能找到）
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wavelengths = [400.0, 443.0, 458.0, 486.0, 500.0, 510.0, 531.0, 547.0, 555.0,
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615.0, 622.0, 629.0, 644.0, 658.0, 665.0, 672.0, 681.0, 686.0,
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700.0, 709.0, 714.0, 715.0, 753.0, 857.0, 900.0]
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fieldnames = ['x_coord', 'y_coord', 'pixel_x', 'pixel_y'] + [f'{w:.6f}' for w in wavelengths]
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with open(path, 'w', newline='', encoding='utf-8-sig') as f:
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writer = csv.DictWriter(f, fieldnames=fieldnames)
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writer.writeheader()
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for i in range(n_points):
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# 模拟水体光谱（典型内陆湖泊反射率 0.005-0.05）
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row = {
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'x_coord': 100.0 + i * 10,
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'y_coord': 30.0 + i * 5,
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'pixel_x': 100 + i,
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'pixel_y': 30 + i,
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}
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for w in wavelengths:
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# 简单合成光谱: 蓝光 < 红光 + 一点叶绿素峰
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base = 0.01 + 0.0001 * (w - 400)
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chl_peak = 0.005 * (1 - abs(w - 560) / 200) if abs(w - 560) < 200 else 0
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row[f'{w:.6f}'] = round(base + chl_peak, 6)
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writer.writerow(row)
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def run_smoke():
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print("=" * 70)
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print("Step 10 散点 CSV 模式 Smoke Test")
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print("=" * 70)
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tmpdir = tempfile.mkdtemp(prefix="step10_smoke_")
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print(f"Tempdir: {tmpdir}")
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sampling_csv = os.path.join(tmpdir, "sampling_spectra.csv")
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output_dir = os.path.join(tmpdir, "10_WaterIndex_CSV")
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os.makedirs(output_dir, exist_ok=True)
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# 1) 创建合成 sampling CSV
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create_synthetic_sampling_csv(sampling_csv, n_points=5)
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print(f"Created sampling CSV: {sampling_csv}")
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with open(sampling_csv, encoding='utf-8-sig') as f:
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header_line = f.readline().strip()
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print(f" header: {header_line[:120]}...")
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# 2) 找到项目自带的 waterindex.csv
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waterindex_csv = PROJECT_ROOT / "src" / "gui" / "model" / "waterindex.csv"
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print(f"Using waterindex.csv: {waterindex_csv}")
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assert waterindex_csv.is_file(), "waterindex.csv not found!"
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# 3) 调 WaterIndexCsvProcessor
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# 注意：父包 __init__.py 顶部有 `from osgeo import gdal, osr`，
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# 在没装 gdal 的 venv 里任何 from ...waterindex_inversion import ... 都会炸。
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# 这里用 importlib 按文件路径直接加载 csv_processor.py 子模块，
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# 完全绕开 __init__.py 的 osgeo 加载。
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import importlib.util as _ilu
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_csv_proc_path = (
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PROJECT_ROOT / "src" / "core" / "algorithms" / "waterindex_inversion"
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/ "csv_processor.py"
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)
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_spec = _ilu.spec_from_file_location("waterindex_csv_processor", _csv_proc_path)
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_mod = _ilu.module_from_spec(_spec)
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sys.modules["waterindex_csv_processor"] = _mod
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_spec.loader.exec_module(_mod)
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WaterIndexCsvProcessor = _mod.WaterIndexCsvProcessor
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progress_log = []
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def progress_cb(msg, pct):
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progress_log.append((msg, pct))
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proc = WaterIndexCsvProcessor(str(waterindex_csv))
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print(f"\n[Step] compute_indices_from_csv...")
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out_files = proc.compute_indices_from_csv(
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sampling_csv_path=sampling_csv,
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output_dir=output_dir,
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selected_formulas=["BGA_Am09KBBI", "BGA_Da052BDA", "BGA_Be16NDPhyI"],
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progress_callback=progress_cb,
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)
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print(f"\n[Result] Generated {len(out_files)} CSV files:")
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for name, path in out_files.items():
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size = os.path.getsize(path)
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print(f" {name:30s} -> {os.path.basename(path)} ({size} bytes)")
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# 4) 验证每个输出 CSV 的列结构
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print(f"\n[Verify] Column structure check:")
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all_pass = True
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import pandas as pd
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for name, path in out_files.items():
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df = pd.read_csv(path, encoding='utf-8-sig')
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cols = list(df.columns)
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expected = ['longitude', 'latitude', name]
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ok = (cols == expected) and (len(df) == 5)
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flag = "✓" if ok else "✗"
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if not ok:
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all_pass = False
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print(f" {flag} {name:30s} cols={cols} rows={len(df)}")
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# 5) 验证坐标重命名
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print(f"\n[Verify] Coordinate rename (x_coord→longitude, y_coord→latitude):")
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sample = pd.read_csv(out_files[list(out_files.keys())[0]], encoding='utf-8-sig')
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print(f" longitude values: {sample['longitude'].tolist()}")
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print(f" latitude values: {sample['latitude'].tolist()}")
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coord_ok = (sample['longitude'].iloc[0] == 100.0 and
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sample['latitude'].iloc[0] == 30.0)
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if not coord_ok:
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all_pass = False
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print(f" {'✓' if coord_ok else '✗'} coordinate rename correct")
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# 6) 验证公式值非 NaN
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print(f"\n[Verify] Formula values (no NaN):")
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for name, path in out_files.items():
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df = pd.read_csv(path, encoding='utf-8-sig')
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col = df[name]
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n_nan = col.isna().sum()
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n_inf = ((col == float('inf')) | (col == float('-inf'))).sum()
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all_nan = col.dropna().empty
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if n_nan > 0 or n_inf > 0 or all_nan:
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print(f" ✗ {name:30s}: NaN={n_nan} Inf={n_inf} empty={all_nan}")
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print(f" values: {col.tolist()}")
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all_pass = False
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else:
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mn, mx = col.min(), col.max()
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print(f" ✓ {name:30s}: range=[{mn:.4f}, {mx:.4f}]")
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# 7) 进度回调检查
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print(f"\n[Verify] Progress callback:")
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print(f" Total progress events: {len(progress_log)}")
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if progress_log:
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first_msg, first_pct = progress_log[0]
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last_msg, last_pct = progress_log[-1]
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print(f" First: ({first_pct:.1f}%) {first_msg}")
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print(f" Last : ({last_pct:.1f}%) {last_msg}")
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progress_ok = (last_pct == 100.0)
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if not progress_ok:
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all_pass = False
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print(f" {'✓' if progress_ok else '✗'} last progress = 100%")
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# 8) 总结
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print(f"\n{'=' * 70}")
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if all_pass:
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print(f"✓ ALL CHECKS PASSED")
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else:
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print(f"✗ SOME CHECKS FAILED — inspect output above")
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print(f"{'=' * 70}")
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# 清理
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shutil.rmtree(tmpdir, ignore_errors=True)
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return all_pass
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if __name__ == "__main__":
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success = run_smoke()
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sys.exit(0 if success else 1)
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@ -649,184 +649,11 @@ class WaterIndexProcessor:
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# ------------------------------------------------------------------
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# 散点处理入口（Step 10 重构后使用，与 Step 9 对称）
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# ------------------------------------------------------------------
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class WaterIndexCsvProcessor:
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"""
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散点 CSV 驱动的水色指数反演器。
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设计目的
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--------
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与 Step 9 (ML 预测) 完全对称的【散点处理模式】：
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* 输入：Step 4 生成的 ``sampling_spectra.csv``，列结构为
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``x_coord, y_coord, pixel_x, pixel_y, 400.000000, 401.000000, ...``
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* 处理：解析 ``waterindex.csv`` 中的公式，对每行采样点
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提取对应波段数值、逐行 eval 计算水色指数
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* 输出：每个公式一个 CSV，列严格为 ``longitude, latitude, <formula_name>``，
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可直接喂给 Step 11 ContentMapper
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输出目录
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--------
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默认 ``{work_dir}/10_WaterIndex_CSV/``；若用户指定 ``output_dir`` 则用其值。
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"""
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COORD_RENAME_MAP = {
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"x_coord": "longitude",
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"y_coord": "latitude",
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"lon": "longitude",
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"lat": "latitude",
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}
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def __init__(self, waterindex_csv_path: Optional[str] = None):
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if waterindex_csv_path is None:
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candidates = [
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os.path.join(os.path.dirname(__file__), '..', '..', 'gui', 'model', 'waterindex.csv'),
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os.path.join(os.path.dirname(__file__), '..', '..', '..', 'gui', 'model', 'waterindex.csv'),
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]
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for p in candidates:
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if os.path.isfile(p):
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waterindex_csv_path = p
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break
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self.waterindex_csv_path = waterindex_csv_path
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self._index_calc = None
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def _get_index_calc(self):
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"""懒加载 WaterQualityIndexCalculator（首次访问时实例化）"""
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if self._index_calc is None and self.waterindex_csv_path:
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from src.utils.water_index import WaterQualityIndexCalculator
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self._index_calc = WaterQualityIndexCalculator(self.waterindex_csv_path)
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return self._index_calc
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@staticmethod
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def _detect_wavelength_columns(df: "pd.DataFrame") -> List[str]:
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"""识别光谱列：列名是浮点数字符串（Step 4 输出的 '400.000000' 形式）"""
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import re
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wl_cols = []
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for col in df.columns:
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try:
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float(str(col).strip())
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wl_cols.append(col)
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except (ValueError, TypeError):
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continue
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return wl_cols
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@staticmethod
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def _safe_filename(name: str) -> str:
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"""公式名 → 文件名安全字符（与旧 BSQ 输出命名习惯一致）"""
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return re.sub(r'[^\w\u4e00-\u9fff-]', '_', name).strip('_') or 'index'
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def compute_indices_from_csv(
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self,
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sampling_csv_path: str,
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output_dir: str,
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selected_formulas: Optional[List[str]] = None,
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progress_callback: Optional[Callable[[str, float], None]] = None,
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) -> Dict[str, str]:
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"""
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散点 CSV → 按指数拆分的多个 CSV。
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Parameters
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----------
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sampling_csv_path : str
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Step 4 输出的 ``sampling_spectra.csv`` 路径
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output_dir : str
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输出目录；不存在会自动创建
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selected_formulas : list, optional
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要计算的公式名列表；None 或空列表 = 全部公式
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progress_callback : callable, optional
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进度回调 ``(msg: str, pct: float)``
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Returns
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-------
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dict
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``{公式名: 输出 CSV 路径}``
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"""
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def notify(msg: str, pct: float) -> None:
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if progress_callback:
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progress_callback(msg, pct)
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if not os.path.isfile(sampling_csv_path):
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raise FileNotFoundError(f"采样点 CSV 不存在: {sampling_csv_path}")
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if not self.waterindex_csv_path or not os.path.isfile(self.waterindex_csv_path):
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raise FileNotFoundError(
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f"waterindex.csv 未配置或不存在: {self.waterindex_csv_path}"
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)
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os.makedirs(output_dir, exist_ok=True)
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notify("正在读取采样点 CSV…", 5)
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import pandas as pd
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df = pd.read_csv(sampling_csv_path, encoding="utf-8-sig")
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if df.empty:
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raise ValueError(f"采样点 CSV 为空: {sampling_csv_path}")
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# 坐标列重命名（x_coord → longitude, y_coord → latitude）
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df = df.rename(columns={k: v for k, v in self.COORD_RENAME_MAP.items()
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if k in df.columns})
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if "longitude" not in df.columns or "latitude" not in df.columns:
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raise ValueError(
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f"采样点 CSV 缺少坐标列（期望 x_coord/y_coord 或 longitude/latitude），"
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f"实际列: {list(df.columns)}"
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)
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# 识别光谱列
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wl_cols = self._detect_wavelength_columns(df)
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if not wl_cols:
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raise ValueError(
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f"采样点 CSV 中未识别到任何光谱列（列名为数字）,"
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f"实际列: {list(df.columns)}"
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)
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notify(f"识别到 {len(wl_cols)} 个光谱列, 采样点 {len(df)} 个", 15)
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calc = self._get_index_calc()
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if calc is None:
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raise RuntimeError("WaterQualityIndexCalculator 初始化失败")
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all_formula_names = calc.list_available()
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if selected_formulas:
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targets = [n for n in selected_formulas if n in all_formula_names]
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missing = [n for n in selected_formulas if n not in all_formula_names]
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if missing:
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print(f"[WaterIndexCsvProcessor] 警告: 以下公式未在 waterindex.csv 中找到,已跳过: {missing}")
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else:
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targets = all_formula_names
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if not targets:
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raise ValueError("没有可计算的公式（selected_formulas 为空且 waterindex.csv 中无公式）")
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# 一次性算出所有目标公式的 Series（避免重复遍历 DataFrame）
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notify(f"开始逐行计算 {len(targets)} 个公式…", 25)
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spectra_df = df[wl_cols]
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try:
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results_df = calc.calculate_many(targets, spectra_df)
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except Exception as e:
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raise RuntimeError(f"公式计算失败: {e}")
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# 每个公式一个 CSV：longitude, latitude, <formula_name>
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out_files: Dict[str, str] = {}
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n_total = len(targets)
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for i, name in enumerate(targets):
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try:
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per_idx = results_df[name]
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out_df = pd.DataFrame({
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"longitude": df["longitude"].values,
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"latitude": df["latitude"].values,
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name: per_idx.values,
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})
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out_path = os.path.join(output_dir, f"{self._safe_filename(name)}.csv")
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out_df.to_csv(out_path, index=False, float_format="%.6f", encoding="utf-8-sig")
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out_files[name] = out_path
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notify(
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f"[{i + 1}/{n_total}] {name} → {os.path.basename(out_path)}",
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25 + 70 * (i + 1) / n_total,
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)
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except Exception as e:
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print(f"[WaterIndexCsvProcessor] 公式 '{name}' 失败: {e}")
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continue
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notify(f"完成！共输出 {len(out_files)} / {n_total} 个指数 CSV", 100)
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return out_files
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# WaterIndexCsvProcessor 已拆出到独立子模块 csv_processor.py，
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# 目的是让纯 CSV 计算链路不再被 __init__.py 顶部 osgeo import 拖垮。
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# 这里做一次 re-export，保留所有 `from src.core.algorithms.waterindex_inversion import WaterIndexCsvProcessor`
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# 这类已有 import 路径仍能正常工作（生产环境/打包后）。
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from src.core.algorithms.waterindex_inversion.csv_processor import WaterIndexCsvProcessor # noqa: E402,F401
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# 保留旧 import 路径兼容
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220
src/core/algorithms/waterindex_inversion/csv_processor.py
Normal file
220
src/core/algorithms/waterindex_inversion/csv_processor.py
Normal file
@ -0,0 +1,220 @@
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# -*- coding: utf-8 -*-
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"""
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水色指数反演 — 散点 CSV 模式处理器（独立子模块）。
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设计意图
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--------
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本模块与 ``waterindex_inversion.__init__.py`` 中的 ``WaterIndexProcessor``
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(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
|
||||
Reference in New Issue
Block a user