181 lines
7.6 KiB
Python
181 lines
7.6 KiB
Python
from sqlalchemy.orm import Session
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from typing import List, Dict, Any, Optional
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from ..models.account import Account
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from ..models.section_data import SectionData
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from ..models.checkpoint import Checkpoint
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from ..models.settlement_data import SettlementData
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from ..models.level_data import LevelData
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from ..services.section_data import SectionDataService
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from ..services.checkpoint import CheckpointService
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from ..services.settlement_data import SettlementDataService
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from ..services.level_data import LevelDataService
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from ..services.account import AccountService
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import pandas as pd
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import logging
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from datetime import datetime
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logger = logging.getLogger(__name__)
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class ExportExcelService:
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def __init__(self):
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self.account_service = AccountService()
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self.section_service = SectionDataService()
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self.checkpoint_service = CheckpointService()
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self.settlement_service = SettlementDataService()
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self.level_service = LevelDataService()
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def get_field_comments(self, model_class) -> Dict[str, str]:
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"""获取模型字段的注释信息"""
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comments = {}
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for column in model_class.__table__.columns:
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if column.comment:
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comments[column.name] = column.comment
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return comments
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def merge_settlement_with_related_data(self,
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db: Session,
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settlement_data: SettlementData,
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section_data: SectionData,
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checkpoint_data: Checkpoint,
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level_data: LevelData) -> Dict[str, Any]:
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"""
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合并沉降数据与关联数据，去除重复和id字段
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"""
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result = {}
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# 沉降数据字段映射（用注释名作为键）
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settlement_comments = self.get_field_comments(SettlementData)
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settlement_dict = settlement_data.to_dict()
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for field_name, value in settlement_dict.items():
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# 跳过id字段
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if field_name == 'id':
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continue
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# 使用注释名作为键，如果没有注释则使用字段名
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key = settlement_comments.get(field_name, field_name)
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result[key] = value
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# 断面数据字段映射（添加前缀）
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section_comments = self.get_field_comments(SectionData)
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section_dict = section_data.to_dict()
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for field_name, value in section_dict.items():
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# 跳过id和account_id字段
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if field_name in ['id', 'account_id']:
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continue
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key = section_comments.get(field_name, field_name)
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result[f"断面_{key}"] = value
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# 观测点数据字段映射（添加前缀）
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checkpoint_comments = self.get_field_comments(Checkpoint)
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checkpoint_dict = checkpoint_data.to_dict()
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for field_name, value in checkpoint_dict.items():
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# 跳过id和section_id字段（section_id可能重复）
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if field_name in ['id', 'section_id']:
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continue
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key = checkpoint_comments.get(field_name, field_name)
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result[f"观测点_{key}"] = value
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# 水准数据字段映射（添加前缀）
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level_comments = self.get_field_comments(LevelData)
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level_dict = level_data.to_dict()
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for field_name, value in level_dict.items():
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# 跳过id和NYID字段（NYID可能重复）
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if field_name in ['id', 'NYID']:
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continue
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key = level_comments.get(field_name, field_name)
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result[f"水准_{key}"] = value
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return result
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def export_settlement_data_to_file(self, db: Session, project_name: str, file_path: str):
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"""
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根据项目名称导出沉降数据Excel文件到指定路径
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"""
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logger.info(f"开始导出项目 '{project_name}' 的沉降数据到文件: {file_path}")
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# 1. 在账号表查询到账号id作为account_id
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account_responses = self.account_service.search_accounts(db, project_name=project_name)
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if not account_responses:
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logger.warning(f"未找到项目名称为 '{project_name}' 的账号")
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raise ValueError(f"未找到项目名称为 '{project_name}' 的账号")
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account_response = account_responses[0]
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account_id = str(account_response.id)
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logger.info(f"找到账号 ID: {account_id}")
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# 2. 通过 account_id 查询断面数据
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sections = self.section_service.search_section_data(db, account_id=account_id, limit=10000)
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if not sections:
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logger.warning(f"账号 {account_id} 下未找到断面数据")
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raise ValueError(f"账号 {account_id} 下未找到断面数据")
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logger.info(f"找到 {len(sections)} 个断面")
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all_settlement_records = []
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# 3-6. 遍历断面数据，查询关联数据
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for section in sections:
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section_id = section.section_id
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logger.debug(f"处理断面: {section_id}")
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# 3. 通过断面数据的section_id查询观测点数据
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checkpoints = self.checkpoint_service.get_by_section_id(db, section_id)
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if not checkpoints:
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logger.debug(f"断面 {section_id} 下未找到观测点数据")
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continue
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for checkpoint in checkpoints:
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point_id = checkpoint.point_id
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# 4. 通过观测点数据的point_id查询沉降数据集
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settlements = self.settlement_service.get_by_point_id(db, point_id)
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if not settlements:
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logger.debug(f"观测点 {point_id} 下未找到沉降数据")
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continue
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for settlement in settlements:
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nyid = settlement.NYID
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# 5. 通过沉降数据的NYID查询水准数据
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level_data_list = self.level_service.get_by_nyid(db, nyid)
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if not level_data_list:
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logger.warning(f"期数 {nyid} 下未找到水准数据")
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# 即使没有水准数据，也继续处理
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level_data = None
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else:
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level_data = level_data_list[0]
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# 6. 合并数据
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merged_record = self.merge_settlement_with_related_data(
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db, settlement, section, checkpoint, level_data
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)
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all_settlement_records.append(merged_record)
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if not all_settlement_records:
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logger.warning("未找到任何沉降数据")
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raise ValueError("未找到任何沉降数据")
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logger.info(f"共找到 {len(all_settlement_records)} 条沉降数据记录")
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# 转换为DataFrame
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df = pd.DataFrame(all_settlement_records)
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# 导出到Excel文件
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with pd.ExcelWriter(file_path, engine='openpyxl') as writer:
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df.to_excel(writer, index=False, sheet_name='沉降数据')
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# 自动调整列宽
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worksheet = writer.sheets['沉降数据']
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for column in worksheet.columns:
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max_length = 0
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column_letter = column[0].column_letter
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for cell in column:
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try:
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if len(str(cell.value)) > max_length:
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max_length = len(str(cell.value))
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except:
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pass
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adjusted_width = min(max_length + 2, 50)
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worksheet.column_dimensions[column_letter].width = adjusted_width
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logger.info("Excel文件生成完成")
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