初始化协作仓库：赛题材料、初步试解与协作说明

- 大文件（附件原始包、data/）由 files.ddwsgood.top 承载，不入库
- 新增 .gitignore/.gitattributes/COLLAB.md

2026_初步评估/B_DeepSeek试解/结果/catalog_audit.json

@@ -0,0 +1,157 @@
+{
+  "script_version": "v2 (2026-10-23 correction)",
+  "source_zip": "3ecd6ef8-fb24-44f0-b9f1-c8839fe83760.zip",
+  "doi": "10.11888/Cryos.tpdc.272812",
+  "metadata_claim": {
+    "slope_failures": 737,
+    "glof": 380
+  },
+  "claim_note": "737/380 是元数据描述；本表只报可复现的实际计数，不为对齐宣传数修改数据。",
+  "sheet_diagnostics": {
+    "Glacier detachments": {
+      "raw_rows": 344,
+      "event_rows": 27,
+      "time_missing": 0,
+      "merged_ranges": 2,
+      "merged_cells_filled": 5
+    },
+    "Ice avalanches": {
+      "raw_rows": 164,
+      "event_rows": 163,
+      "time_missing": 0,
+      "merged_ranges": 0,
+      "merged_cells_filled": 0
+    },
+    "Ice-rock avalanches": {
+      "raw_rows": 91,
+      "event_rows": 91,
+      "time_missing": 0,
+      "merged_ranges": 0,
+      "merged_cells_filled": 0
+    },
+    "Rock avalanches": {
+      "raw_rows": 469,
+      "event_rows": 469,
+      "time_missing": 0,
+      "merged_ranges": 0,
+      "merged_cells_filled": 0
+    },
+    "GLOF triggered by avalanches": {
+      "raw_rows": 64,
+      "event_rows": 64,
+      "time_missing": 0,
+      "merged_ranges": 0,
+      "merged_cells_filled": 0
+    },
+    "GLOF triggered by calving": {
+      "raw_rows": 38,
+      "event_rows": 38,
+      "time_missing": 0,
+      "merged_ranges": 0,
+      "merged_cells_filled": 0
+    }
+  },
+  "slope_failures": {
+    "raw": {
+      "n": 852,
+      "by_sheet": {
+        "Rock avalanches": 469,
+        "Ice avalanches": 163,
+        "Ice-rock avalanches": 91,
+        "GLOF triggered by avalanches": 64,
+        "GLOF triggered by calving": 38,
+        "Glacier detachments": 27
+      },
+      "by_mechanism": {
+        "Rock avalanche": 469,
+        "Ice avalanche": 163,
+        "Ice-rock avalanche": 91,
+        "Ice avalanches": 50,
+        "Glacier terminal calving": 38,
+        "Glacier detachment": 27,
+        "Ice-rock avalanches": 14
+      },
+      "by_precision": {
+        "day": 377,
+        "range": 305,
+        "year": 114,
+        "other": 54,
+        "month": 2
+      },
+      "hma": 148,
+      "himalaya": 88
+    },
+    "candidate_unique": {
+      "n": 781,
+      "by_sheet": {
+        "Rock avalanches": 463,
+        "Ice avalanches": 162,
+        "Ice-rock avalanches": 91,
+        "GLOF triggered by calving": 38,
+        "Glacier detachments": 27
+      },
+      "by_mechanism": {
+        "Rock avalanche": 463,
+        "Ice avalanche": 162,
+        "Ice-rock avalanche": 91,
+        "Glacier terminal calving": 38,
+        "Glacier detachment": 27
+      },
+      "by_precision": {
+        "day": 345,
+        "range": 291,
+        "year": 105,
+        "other": 38,
+        "month": 2
+      },
+      "hma": 120,
+      "himalaya": 63
+    },
+    "duplicates": {
+      "exact_removed": 32,
+      "suspect_year_only": 39
+    }
+  },
+  "slope_fields": {
+    "volume": 561,
+    "volume_and_H": 440,
+    "angle": 408
+  },
+  "glof": {
+    "raw": {
+      "n": 376,
+      "by_sheet": {},
+      "by_mechanism": {},
+      "by_precision": {
+        "day": 151,
+        "other": 100,
+        "range": 98,
+        "year": 26,
+        "month": 1
+      },
+      "hma": 108,
+      "himalaya": 74
+    },
+    "candidate_unique": {
+      "n": 376,
+      "by_sheet": {},
+      "by_mechanism": {},
+      "by_precision": {
+        "day": 151,
+        "other": 100,
+        "range": 98,
+        "year": 26,
+        "month": 1
+      },
+      "hma": 108,
+      "himalaya": 74
+    },
+    "duplicates": {
+      "exact_removed": 0,
+      "suspect_year_only": 0
+    }
+  },
+  "glof_fields": {
+    "volume": 68
+  }
+}

2026_初步评估/B_DeepSeek试解/结果/catalog_log.txt

@@ -0,0 +1,148 @@
+/home/ddwsgood/MathModelComp/MathorCup2026/2026_初步评估/B_DeepSeek试解/源码/audit_catalog.py:145: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+A typical example is when you are setting values in a column of a DataFrame, like:
+
+df["col"][row_indexer] = value
+
+Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+
+See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+
+  sf[c] = pd.to_numeric(sf[c], errors="coerce")
+/home/ddwsgood/MathModelComp/MathorCup2026/2026_初步评估/B_DeepSeek试解/源码/audit_catalog.py:146: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+A typical example is when you are setting values in a column of a DataFrame, like:
+
+df["col"][row_indexer] = value
+
+Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+
+See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+
+  sf["trigger"] = sf.trigger_plural.where(sf.trigger_plural.notna(), sf.trigger_singular)
+/home/ddwsgood/MathModelComp/MathorCup2026/2026_初步评估/B_DeepSeek试解/源码/audit_catalog.py:152: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+A typical example is when you are setting values in a column of a DataFrame, like:
+
+df["col"][row_indexer] = value
+
+Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+
+See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+
+  sf["dedupe_key_exact"] = np.where(
+/home/ddwsgood/MathModelComp/MathorCup2026/2026_初步评估/B_DeepSeek试解/源码/audit_catalog.py:156: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+A typical example is when you are setting values in a column of a DataFrame, like:
+
+df["col"][row_indexer] = value
+
+Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+
+See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+
+  sf["dedupe_key_year"] = np.where(
+/home/ddwsgood/MathModelComp/MathorCup2026/2026_初步评估/B_DeepSeek试解/源码/audit_catalog.py:160: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+A typical example is when you are setting values in a column of a DataFrame, like:
+
+df["col"][row_indexer] = value
+
+Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+
+See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+
+  sf["is_dupe_exact"] = sf.dedupe_key_exact.notna() & sf.duplicated("dedupe_key_exact", keep="first")
+/home/ddwsgood/MathModelComp/MathorCup2026/2026_初步评估/B_DeepSeek试解/源码/audit_catalog.py:161: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+A typical example is when you are setting values in a column of a DataFrame, like:
+
+df["col"][row_indexer] = value
+
+Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+
+See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+
+  sf["is_dupe_suspect"] = (~sf.is_dupe_exact) & sf.dedupe_key_year.notna() & sf.duplicated("dedupe_key_year", keep="first")
+/home/ddwsgood/MathModelComp/MathorCup2026/2026_初步评估/B_DeepSeek试解/源码/audit_catalog.py:162: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+A typical example is when you are setting values in a column of a DataFrame, like:
+
+df["col"][row_indexer] = value
+
+Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+
+See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+
+  sf["candidate_unique"] = ~(sf.is_dupe_exact | sf.is_dupe_suspect)
+/home/ddwsgood/MathModelComp/MathorCup2026/2026_初步评估/B_DeepSeek试解/源码/audit_catalog.py:164: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+A typical example is when you are setting values in a column of a DataFrame, like:
+
+df["col"][row_indexer] = value
+
+Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+
+See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+
+  sf["is_hma"] = sf.lat.between(25, 45) & sf.lon.between(60, 105)
+/home/ddwsgood/MathModelComp/MathorCup2026/2026_初步评估/B_DeepSeek试解/源码/audit_catalog.py:165: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+A typical example is when you are setting values in a column of a DataFrame, like:
+
+df["col"][row_indexer] = value
+
+Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+
+See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+
+  sf["is_himalaya"] = sf.lat.between(26, 31) & sf.lon.between(75, 95)
+/home/ddwsgood/MathModelComp/MathorCup2026/2026_初步评估/B_DeepSeek试解/源码/audit_catalog.py:187: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+A typical example is when you are setting values in a column of a DataFrame, like:
+
+df["col"][row_indexer] = value
+
+Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+
+See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+
+  gl["is_hma"] = gl.lat.between(25, 45) & gl.lon.between(60, 105)
+/home/ddwsgood/MathModelComp/MathorCup2026/2026_初步评估/B_DeepSeek试解/源码/audit_catalog.py:188: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+A typical example is when you are setting values in a column of a DataFrame, like:
+
+df["col"][row_indexer] = value
+
+Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+
+See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+
+  gl["is_himalaya"] = gl.lat.between(26, 31) & gl.lon.between(75, 95)
+== sheet diagnostics
+  Glacier detachments                raw= 344 events=  27 no_time=   0 merged_ranges=2 merged_cells=5
+  Ice avalanches                     raw= 164 events= 163 no_time=   0 merged_ranges=0 merged_cells=0
+  Ice-rock avalanches                raw=  91 events=  91 no_time=   0 merged_ranges=0 merged_cells=0
+  Rock avalanches                    raw= 469 events= 469 no_time=   0 merged_ranges=0 merged_cells=0
+  GLOF triggered by avalanches       raw=  64 events=  64 no_time=   0 merged_ranges=0 merged_cells=0
+  GLOF triggered by calving          raw=  38 events=  38 no_time=   0 merged_ranges=0 merged_cells=0
+
+== slope failures
+ raw: {'n': 852, 'by_sheet': {'Rock avalanches': 469, 'Ice avalanches': 163, 'Ice-rock avalanches': 91, 'GLOF triggered by avalanches': 64, 'GLOF triggered by calving': 38, 'Glacier detachments': 27}, 'by_mechanism': {'Rock avalanche': 469, 'Ice avalanche': 163, 'Ice-rock avalanche': 91, 'Ice avalanches': 50, 'Glacier terminal calving': 38, 'Glacier detachment': 27, 'Ice-rock avalanches': 14}, 'by_precision': {'day': 377, 'range': 305, 'year': 114, 'other': 54, 'month': 2}, 'hma': 148, 'himalaya': 88}
+ cand-unique: {'n': 781, 'by_sheet': {'Rock avalanches': 463, 'Ice avalanches': 162, 'Ice-rock avalanches': 91, 'GLOF triggered by calving': 38, 'Glacier detachments': 27}, 'by_mechanism': {'Rock avalanche': 463, 'Ice avalanche': 162, 'Ice-rock avalanche': 91, 'Glacier terminal calving': 38, 'Glacier detachment': 27}, 'by_precision': {'day': 345, 'range': 291, 'year': 105, 'other': 38, 'month': 2}, 'hma': 120, 'himalaya': 63}
+ dup: {'exact_removed': 32, 'suspect_year_only': 39}
+
+== GLOF
+ raw: {'n': 376, 'by_sheet': {}, 'by_mechanism': {}, 'by_precision': {'day': 151, 'other': 100, 'range': 98, 'year': 26, 'month': 1}, 'hma': 108, 'himalaya': 74}
+ cand-unique: {'n': 376, 'by_sheet': {}, 'by_mechanism': {}, 'by_precision': {'day': 151, 'other': 100, 'range': 98, 'year': 26, 'month': 1}, 'hma': 108, 'himalaya': 74}
+
+============================================================
+体积+距离齐全（候选唯一）：
+sheet
+Glacier detachments      8
+Ice avalanches          14
+Ice-rock avalanches     21
+Rock avalanches        393
+留出：log10H=0.510+0.131log10V，RMSE=0.41，R²=-0.59（n_test=9）
+角 of reach：n=404，p10=10.5°，中位=19.4°，p90=27.5°
+等效 tan： {'p10': 0.185, 'median': 0.352, 'p90': 0.521}

2026_初步评估/B_DeepSeek试解/结果/run_log.txt

@@ -0,0 +1,79 @@
+======================================================================
+Q1 候选特征百分位基线
+======================================================================
+   feature    unit  event_value  pctl_vs_season  n_control  extremeness
+    netrad    W/m2      356.232          96.585        205       46.585
+  dVWC_24h m3/m3/d       -0.011           4.878        205       45.122
+     Ta_2m    degC       11.720          93.659        205       43.659
+precip_72h  mm/72h        0.000          10.732        205       39.268
+  dpres_3h  hPa/3h        0.600          15.854        205       34.146
+     RH_2m       %       69.400          19.512        205       30.488
+ soilT_0.1    degC       17.038          78.537        205       28.537
+   dT_2_10    degC        0.340          76.341        205       26.341
+precip_24h  mm/24h        0.000          27.073        205       22.927
+shear_1_20     1/s        0.013          29.268        205       20.732
+   VWC_0.1   m3/m3        0.083          56.098        205        6.098
+ precip_3h   mm/3h        0.000          46.341        205        3.659
+ precip_1h    mm/h        0.000          48.293        205        1.707
+    WS_10m     m/s        0.785          48.293        205        1.707
+
+按年 bootstrap 排序稳定性（500 次，5 年有放回）：
+   feature  rank_mean  rank_p05  rank_p95  top5_freq
+    netrad       1.31      1.00      2.00       1.00
+  dVWC_24h       2.09      1.00      3.00       1.00
+     Ta_2m       2.74      2.00      4.00       1.00
+precip_72h       4.14      4.00      5.00       1.00
+  dpres_3h       5.31      5.00      6.00       0.69
+     RH_2m       6.53      6.00      8.00       0.00
+ soilT_0.1       7.20      3.00     11.00       0.31
+   dT_2_10       7.80      7.00      9.00       0.00
+precip_24h       8.86      7.00     10.00       0.00
+shear_1_20       9.65      9.00     11.00       0.00
+   VWC_0.1      10.89      7.00     14.00       0.00
+ precip_3h      12.18     11.00     13.00       0.00
+    WS_10m      12.78     11.00     14.00       0.00
+ precip_1h      13.51     13.00     14.00       0.00
+
+======================================================================
+数据核查：附件冰川 NetCDF 与本底 DEM / 公开 AOI 的坐标一致性
+======================================================================
+nc 质心 lon=85.52770 lat=28.28802；公开 AOI bbox lon[85.5050,85.5400] lat[28.2620,28.2920]；质心是否落在 AOI 内：True
+重叠格点 20010；本地 DEM 均值 4977 m，nc 均值 4991 m；差值中位 +29.0 m，MAE 43.6 m
+
+======================================================================
+Q4 情景传播基线：真实 DEM 上 D8 路径 + Voellmy 型一维运动积分
+======================================================================
+源区中心 (lon=85.5190, lat=28.2740) -> 行 261, 列 296, DEM 高程 5017 m, 坡度 22.3°
+路径格点 54，水平长度 20.62 km，落差 2746 m，末端高程 1913 m
+ mu  xi_m_s2  h_m  v_max_m_s  t_total_s  t_total_min  v_end_m_s  runout_km
+0.1     2000 25.0       99.7      730.8         12.2        0.5       20.6
+0.1     1000 25.0       84.9     3259.6         54.3        0.5       20.6
+0.1     2000 10.0       79.0     4078.6         68.0        0.5       20.6
+0.1      500 25.0       69.4     5760.7         96.0        0.5       20.6
+0.1     1000 10.0       64.9     6571.7        109.5        0.5       20.6
+0.1      500 10.0       50.2     9137.1        152.3        0.5       20.6
+0.1     2000 25.0       93.0     9952.5        165.9        0.5       20.6
+0.1     1000 25.0       79.3    11473.4        191.2        0.5       20.6
+0.1     2000 10.0       73.8    11911.0        198.5        0.5       20.6
+0.1      500 25.0       65.9    13785.1        229.8        0.5       20.6
+0.1     1000 10.0       61.8    14617.0        243.6        0.5       20.6
+0.1      500 10.0       48.1    17978.8        299.6        0.5       20.6
+0.2      500 10.0       43.6    20000.0        333.3        0.0       20.6
+0.2      500 25.0       58.3    20000.0        333.3        0.0       20.6
+0.2     1000 10.0       55.0    20000.0        333.3        0.0       20.6
+0.2     1000 25.0       66.8    20000.0        333.3        0.0       20.6
+0.2     2000 10.0       64.4    20000.0        333.3        0.0       20.6
+0.2     2000 25.0       78.2    20000.0        333.3        0.0       20.6
+
+代表情景 (μ=0.10, ξ=1000, h=25 m) 每 2 km 到达时间：
+  km  t_min  v_m_s   elev
+ 2.1    0.8   79.3 3727.1
+ 4.2    1.4   42.2 3450.7
+ 6.0    2.5   49.2 3162.9
+ 7.9    3.3   19.1 2932.2
+10.2    4.6   23.8 2603.4
+12.0   14.4   32.6 2366.2
+14.1   32.8   17.2 2213.3
+15.9   71.0    0.5 2139.3
+18.0  119.1    0.5 2003.4
+19.8  171.1   21.4 1916.6

2026_初步评估/B_DeepSeek试解/结果/runout_angle_stats.json

@@ -0,0 +1,24 @@
+{
+  "script_version": "v2",
+  "n_ice": 29,
+  "n_ice_hma": 7,
+  "fit": [
+    0.5103912274801464,
+    0.1305365071066225
+  ],
+  "holdout_rmse_log10": 0.4098753489469039,
+  "holdout_r2": -0.5948355536248682,
+  "n_test": 9,
+  "angle_reach_deg": {
+    "n": 404,
+    "p10": 10.5,
+    "median": 19.4,
+    "p90": 27.5
+  },
+  "mu_eq_tan": {
+    "p10": 0.18533904493153439,
+    "median": 0.3521555902892187,
+    "p90": 0.5205670505517462
+  },
+  "note_2026": "2026 事件的 8.85°（=atan(3210/20620)）来自‘模拟/粗网格几何’，仅作外部量级对照，不能据此断言真实事件的分位或反推 μ 范围。"
+}

2026_初步评估/B_DeepSeek试解/结果/tpdc_increment_log.txt

@@ -0,0 +1,74 @@
+/home/ddwsgood/MathModelComp/MathorCup2026/2026_初步评估/B_DeepSeek试解/源码/audit_tpdc_increment.py:170: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+A typical example is when you are setting values in a column of a DataFrame, like:
+
+df["col"][row_indexer] = value
+
+Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+
+See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+
+  path["dist_to_river_km"] = res
+======================================================================
+1) GFS 灾前预报（起报 2026-08-25 00Z）
+nc=吉隆县GFS_降水云与水成物_20260825T00Z_f000-f072.nc
+起报=2026-08-25T00:00:00Z 时次=[0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72]
+网格 lat28.250-29.250 lon84.500-86.250
+  site  grid_lat  grid_lon  fh        valid_utc  precip_rate_mm_h  precip_6h_mm  precip_cum_mm  cloud_water_path  precipitable_water
+source     28.25     85.50   0 2026-08-25T00:00              0.00          0.00           0.00              0.01               15.31
+source     28.25     85.50   6 2026-08-25T06:00              0.51          0.56           0.56              0.29               16.71
+source     28.25     85.50  12 2026-08-25T12:00              0.04          1.00           1.56              0.14               16.90
+source     28.25     85.50  18 2026-08-25T18:00              0.01          0.12           1.75              0.06               17.48
+source     28.25     85.50  24 2026-08-26T00:00              0.00          0.00           1.75              0.09               15.71
+source     28.25     85.50  30 2026-08-26T06:00              0.67          0.56           2.31              0.38               16.89
+source     28.25     85.50  36 2026-08-26T12:00              0.54          3.06           5.38              1.42               19.05
+source     28.25     85.50  42 2026-08-26T18:00              1.29         11.00          16.31              0.62               18.60
+source     28.25     85.50  48 2026-08-27T00:00              0.55          3.31          19.69              0.94               18.20
+source     28.25     85.50  54 2026-08-27T06:00              0.10          1.69          21.38              0.39               18.43
+source     28.25     85.50  60 2026-08-27T12:00              0.07          1.44          22.81              0.29               17.85
+source     28.25     85.50  66 2026-08-27T18:00              0.00          0.06          22.88              0.03               17.49
+source     28.25     85.50  72 2026-08-28T00:00              0.00          0.06          22.94              0.05               16.96
+  port     28.25     85.50   0 2026-08-25T00:00              0.00          0.00           0.00              0.01               15.31
+  port     28.25     85.50   6 2026-08-25T06:00              0.51          0.56           0.56              0.29               16.71
+  port     28.25     85.50  12 2026-08-25T12:00              0.04          1.00           1.56              0.14               16.90
+  port     28.25     85.50  18 2026-08-25T18:00              0.01          0.12           1.75              0.06               17.48
+  port     28.25     85.50  24 2026-08-26T00:00              0.00          0.00           1.75              0.09               15.71
+  port     28.25     85.50  30 2026-08-26T06:00              0.67          0.56           2.31              0.38               16.89
+  port     28.25     85.50  36 2026-08-26T12:00              0.54          3.06           5.38              1.42               19.05
+  port     28.25     85.50  42 2026-08-26T18:00              1.29         11.00          16.31              0.62               18.60
+  port     28.25     85.50  48 2026-08-27T00:00              0.55          3.31          19.69              0.94               18.20
+  port     28.25     85.50  54 2026-08-27T06:00              0.10          1.69          21.38              0.39               18.43
+  port     28.25     85.50  60 2026-08-27T12:00              0.07          1.44          22.81              0.29               17.85
+  port     28.25     85.50  66 2026-08-27T18:00              0.00          0.06          22.88              0.03               17.49
+  port     28.25     85.50  72 2026-08-28T00:00              0.00          0.06          22.94              0.05               16.96
+
+事件当日 00-12 UTC 预报（源区/口岸）：
+  site  fh        valid_utc  precip_rate_mm_h  precip_6h_mm
+source  24 2026-08-26T00:00           0.00288        0.0000
+source  30 2026-08-26T06:00           0.67392        0.5625
+source  36 2026-08-26T12:00           0.54144        3.0625
+  port  24 2026-08-26T00:00           0.00288        0.0000
+  port  30 2026-08-26T06:00           0.67392        0.5625
+  port  36 2026-08-26T12:00           0.54144        3.0625
+source: 2026-08-26 00-18UTC 的 6h 累计之和 = 14.6 mm（仅 6h 量求和）
+port: 2026-08-26 00-18UTC 的 6h 累计之和 = 14.6 mm（仅 6h 量求和）
+
+======================================================================
+2) TPCSM 5 km 模拟
+nc 文件数: 5
+file: dpre_20260822-0827.nc
+dims: {'XTIME': 120, 'west_east': 210, 'south_north': 180}
+  XTIME                 ('XTIME',) (120,) float32 minutes since 2026-08-21 06:00:00
+  XLONG                 ('south_north', 'west_east') (180, 210) float32 degree_east
+  XLAT                  ('south_north', 'west_east') (180, 210) float32 degree_north
+  RAINNC                ('XTIME', 'south_north', 'west_east') (120, 180, 210) float32 mm
+attrs: {'CDI': 'Climate Data Interface version 1.9.5 (http://mpimet.mpg.de/cdi)', 'history': 'Sat Aug 29 14:43:07 2026: cdo -cat dpre_2026-08-22_01.nc dpre_2026-08-22_02.nc dpre_2026-08-22_03.nc dpre_2026-08-22_04.nc dpre_2026-08-22_05.nc dpre_2026-08-22_06.nc dpre_2026-08-22_07.nc dpre_2026-08-22_08.nc dpre_2026-08-22_09.nc dpre_2026-08-22_10.nc dpre_2026-08-22_11.nc dpre_2026-08-22_12.nc dpre_2026-08-22_13.nc dpre_2026-08-22_14.nc dpre_2026-08-22_15.nc dpre_2026-08-22_16.nc dpre_2026-08-22_17.nc dpre_2026-08-22_18.nc dpre_2026-08-22_19.nc dpre_2026-08-22_20.nc dpre_2026-08-22_21.nc dpre_2026-08-22_22.nc dpre_2026-08-22_23.nc dpre_2026-08-23_00.nc dpre_2026-08-23_01.nc dpre_2026-08-23_02.nc dpre_2026-08-23_03.nc dpre_2026-08-23_04.nc dpre_2026-08-23_05.nc dpre_2026-08-23_06.nc dpre_2026-08-23_07.nc dpre_2026-08-23_08.nc dpre_2026-08-23_09.nc dpre_2026-08-23_10.nc dpre_2026-08-23_11.nc dpre_2026-08-23_12.nc dpre_2026-08-23_13.nc dpre_2026-08-23_14.nc dpre_2026-08-23_15.nc dpre_2026-08-23_16.nc dpre_2026-08-23_17.nc dpre_2026-08-23_18.nc dpre_2026-08-23_19.nc dpre_2026-08-23_20.nc dpre_2026-08-23_21.nc dpre_2026-08-23_22.nc dpre_2026-08-23_23.nc dpre_2026-08-24_00.nc dpre_2026-08-24_01.nc dpre_2026-08-24_02.nc dpre_2026-08-24_03.nc dpre_2026-08-24_04.nc dpre_2026-08-24_05.nc dpre_2026-08-24_06.nc dpre_2026-08-24_07.nc dpre_2026-08-24_08.nc dpre_2026-08-24_09.nc dpre_2026-08-24_10.nc dpre_2026-08-24_11.nc dpre_2026-08-24_12.nc dpre_2026-08-24_13.nc dpre_2026-08-24_14.nc dpre_2026-08-24_15.nc dpre_2026-08-24_16.nc dpre_2026-08-24_17.nc dpre_2026-08-24_18.nc dpre_2026-08-24_19.nc dpre_2026-08-24_20.nc dpre_2026-08-24_21.nc dpre_2026-08-24_22.nc dpre_2026-08-24_23.nc dpre_2026-08-25_00.nc dpre_2026-08-25_01.nc dpre_2026-08-25_02.nc dpre_2026-08-25_03.nc dpre_2026-08-25_04.nc dpre_2026-08-25_05.nc dpre_2026-08-25_06.nc dpre_2026-08-25_07.nc dpre_2026-08-25_08.nc dpre_2026-08-25_09.nc dpre_2026-08-25_10.nc dpre_2026-08-25_11.nc dpre_2026-08-25_12.nc dpre_2026-08-25_13.nc dpre_2026-08-25_14.nc dpre_2026-08-25_15.nc dpre_2026-08-25_16.nc dpre_2026-08-25_17.nc dpre_2026-08-25_18.nc dpre_2026-08-25_19.nc dpre_2026-08-25_20.nc dpre_2026-08-25_21.nc dpre_2026-08-25_22.nc dpre_2026-08-25_23.nc dpre_2026-08-26_00.nc dpre_2026-08-26_01.nc dpre_2026-08-26_02.nc dpre_2026-08-26_03.nc dpre_2026-08-26_04.nc dpre_2026-08-26_05.nc dpre_2026-08-26_06.nc dpre_2026-08-26_07.nc dpre_2026-08-26_08.nc dpre_2026-08-26_09.nc dpre_2026-08-26_10.nc dpre_2026-08-26_11.nc dpre_2026-08-26_12.nc dpre_2026-08-26_13.nc dpre_2026-08-26_14.nc dpre_2026-08-26_15.nc dpre_2026-08-26_16.nc dpre_2026-08-26_17.nc dpre_2026-08-26_18.nc dpre_2026-08-26_19.nc dpre_2026-08-26_20.nc dpre_2026-08-26_21.nc dpre_2026-08-26_22.nc dpre_2026-08-26_23.nc dpre_2026-08-27_00.nc dpre_20260826-0827.nc\nSat Aug 29 14:00:16 2026: cdo -sub prec_22-26/rainnc_2026-08-22_01.nc prec_22-26/rainnc_2026-08-22_00.nc dpre_2026-08-22_01.nc\nFri Aug 28 19:05:40 2026: cdo -selname,RAINNC data/wrfout_d02_2026-08-22_01:00:00 rainnc_2026-08-22_01.nc', 'Conventions': 'CF-1.6', 'TITLE': ' OUTPUT FROM WRF V4.0 MODEL', 'START_DATE': '2026-08-21_06:00:00', 'SIMULATION_START_DATE': '2026-08-21_06:00:00', 'WEST-EAST_GRID_DIMENSION': np.int32(211), 'SOUTH-NORTH_GRID_DIMENSION': np.int32(181), 'BOTTOM-TOP_GRID_DIMENSION': np.int32(50), 'DX': np.float32(5558.875), 'DY': np.float32(5558.875), 'AERCU_OPT': np.int32(0), 'AERCU_FCT': np.float32(1.0), 'IDEAL_CASE': np.int32(0), 'DIFF_6TH_SLOPEOPT': np.int32(0), 'AUTO_LEVELS_OPT': np.int32(2), 'DIFF_6TH_THRESH': np.float32(0.1), 'DZBOT': np.float32(50.0), 'DZSTRETCH_S': np.float32(1.3), 'DZSTRETCH_U': np.float32(1.1), 'SKEBS_ON': np.int32(0), 'SPEC_BDY_FINAL_MU': np.int32(1), 'USE_Q_DIABATIC': np.int32(0), 'GRIDTYPE': 'C', 'DIFF_OPT': np.int32(1), 'KM_OPT': np.int32(4), 'DAMP_OPT': np.int32(1), 'DAMPCOEF': np.float32(0.2), 'KHDIF': np.float32(0.0), 'KVDIF': np.float32(0.0), 'MP_PHYSICS': np.int32(8), 'RA_LW_PHYSICS': np.int32(3), 'RA_SW_PHYSICS': np.int32(3), 'SF_SFCLAY_PHYSICS': np.int32(1), 'SF_SURFACE_PHYSICS': np.int32(4), 'BL_PBL_PHYSICS': np.int32(9), 'CU_PHYSICS': np.int32(0), 'SF_LAKE_PHYSICS': np.int32(0), 'SURFACE_INPUT_SOURCE': np.int32(1), 'SST_UPDATE': np.int32(1), 'GRID_FDDA': np.int32(0), 'GFDDA_INTERVAL_M': np.int32(0), 'GFDDA_END_H': np.int32(0), 'GRID_SFDDA': np.int32(0), 'SGFDDA_INTERVAL_M': np.int32(0), 'SGFDDA_END_H': np.int32(0), 'HYPSOMETRIC_OPT': np.int32(2), 'USE_THETA_M': np.int32(1), 'GWD_OPT': np.int32(0), 'SF_URBAN_PHYSICS': np.int32(0), 'SF_SURFACE_MOSAIC': np.int32(0), 'SF_OCEAN_PHYSICS': np.int32(0), 'SHCU_PHYSICS': np.int32(2), 'MFSHCONV': np.int32(0), 'FEEDBACK': np.int32(0), 'SMOOTH_OPTION': np.int32(0), 'SWRAD_SCAT': np.float32(1.0), 'W_DAMPING': np.int32(1), 'RADT': np.float32(20.0), 'BLDT': np.float32(0.0), 'CUDT': np.float32(0.0), 'AER_OPT': np.int32(0), 'SWINT_OPT': np.int32(0), 'AER_TYPE': np.int32(1), 'AER_AOD550_OPT': np.int32(1), 'AER_ANGEXP_OPT': np.int32(1), 'AER_SSA_OPT': np.int32(1), 'AER_ASY_OPT': np.iLine truncated
+
+======================================================================
+3) 受灾位置点 / 河网 vs 我们的路径
+受灾点位 shp 类型 1 点数 1 坐标 [(85.37751083772079, 28.278553539886374)]
+河网线 11 条，节点 2138，bbox 85.1898-85.6484E 28.2455-28.5481N
+路径终点 (85.3842,28.2855) 高程 1913 m；受灾点 (85.3775,28.2786)；距离 1.01 km
+路径到最近河网节点：中位 0.163 km，p90 4.138 km，最大 5.205 km
+前 6 个路径点的最近河距 (km): [5.205, 5.12, 4.879, 4.679, 4.5, 4.203]
+后 6 个路径点的最近河距 (km): [0.152, 0.037, 0.06, 0.195, 0.124, 0.22]