Merge branch 'main' of http://119.6.225.4:3000/lhx/project
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@@ -11,7 +11,7 @@ Parquet数据处理与Excel导出脚本
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- 数据关联链:断面→观测点→沉降→水准→原始
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3. 以水准数据为主体整理数据
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- 拆分的benchmarkids(起始点/终止点)
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- 收集测点(同一水准线路的所有观测点)
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- 收集测点(同一水准线路的所有观测点名称aname)
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- 计算时间范围(原始数据mtime范围)
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- 格式化日期(YYYY-MM-DD)
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4. 导出为Excel文件
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@@ -28,7 +28,14 @@ pip install pandas numpy openpyxl
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作者:Claude Code
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日期:2025-11-08
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版本:1.0
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版本:1.2.1
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更新日志:
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- v1.2.1: 测点列优化:从point_id改为aname(观测点名称)
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- v1.2.0: 彻底修复numpy array布尔值判断错误
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- v1.2: 新增NYID期数ID重复检查功能
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- v1.1: 新增数据质量检验机制
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- v1.0: 初始版本
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"""
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import os
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@@ -338,7 +345,7 @@ def process_folder_data(folder_name, folder_path, files):
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else:
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work_site = ""
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# 6. 收集同一水准线路编码的所有水准数据对应的沉降数据,进而获取观测点
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# 6. 收集同一水准线路编码的所有水准数据对应的沉降数据,进而获取观测点名称
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# 找到所有具有相同linecode的水准数据
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same_line_levels = level_df[level_df["linecode"] == linecode]
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same_line_nyids = same_line_levels["NYID"].unique()
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@@ -348,7 +355,18 @@ def process_folder_data(folder_name, folder_path, files):
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# 获取这些沉降数据对应的观测点point_id
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all_point_ids = all_settlements_same_line["point_id"].unique()
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point_ids_str = ",".join(map(str, sorted(all_point_ids)))
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# 从观测点数据中获取对应的aname(观测点名称)
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if not checkpoint_df.empty:
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related_checkpoints_for_names = checkpoint_df[checkpoint_df["point_id"].isin(all_point_ids)]
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if not related_checkpoints_for_names.empty:
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# 获取所有的aname
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all_anames = related_checkpoints_for_names["aname"].dropna().unique()
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anames_str = ",".join(sorted(map(str, all_anames)))
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else:
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anames_str = ""
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else:
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anames_str = ""
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# 7. 计算时间范围(通过同一水准线路编码的所有NYID)
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if has_original:
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@@ -364,7 +382,7 @@ def process_folder_data(folder_name, folder_path, files):
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"水准线路": linecode,
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"起始点": start_point,
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"终止点": end_point,
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"测点": point_ids_str,
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"测点": anames_str, # 修改为观测点名称
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"起始时间": min_mtime,
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"终止时间": max_mtime,
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"类型": work_site
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