420 lines
18 KiB
Python
420 lines
18 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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合并采购单程序
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-------------------
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将多个采购单Excel文件合并成一个文件。
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"""
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import os
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import sys
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import logging
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import pandas as pd
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import xlrd
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import xlwt
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple, Union, Any
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from datetime import datetime
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import random
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from xlutils.copy import copy as xlcopy
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import time
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import json
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import re
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# 配置日志
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logger = logging.getLogger(__name__)
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if not logger.handlers:
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log_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'logs', 'merge_purchase_orders.log')
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os.makedirs(os.path.dirname(log_file), exist_ok=True)
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s',
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handlers=[
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logging.FileHandler(log_file, encoding='utf-8'),
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logging.StreamHandler(sys.stdout)
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]
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)
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logger = logging.getLogger(__name__)
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logger.info("初始化日志系统")
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class PurchaseOrderMerger:
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"""
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采购单合并器：将多个采购单Excel文件合并成一个文件
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"""
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def __init__(self, output_dir="output"):
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"""
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初始化采购单合并器，并设置输出目录
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"""
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logger.info("初始化PurchaseOrderMerger")
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self.output_dir = output_dir
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# 确保输出目录存在
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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logger.info(f"创建输出目录: {output_dir}")
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# 设置路径
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self.template_path = os.path.join("templets", "银豹-采购单模板.xls")
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# 检查模板文件是否存在
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if not os.path.exists(self.template_path):
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logger.error(f"模板文件不存在: {self.template_path}")
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raise FileNotFoundError(f"模板文件不存在: {self.template_path}")
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# 用于记录已处理的文件
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self.cache_file = os.path.join(output_dir, "merged_files.json")
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self.merged_files = self._load_merged_files()
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logger.info(f"初始化完成，模板文件: {self.template_path}")
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def _load_merged_files(self):
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"""加载已合并文件的缓存"""
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if os.path.exists(self.cache_file):
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try:
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with open(self.cache_file, 'r', encoding='utf-8') as f:
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cache = json.load(f)
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logger.info(f"加载已合并文件缓存，共{len(cache)} 条记录")
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return cache
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except Exception as e:
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logger.warning(f"读取缓存文件失败: {e}")
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return {}
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def _save_merged_files(self):
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"""保存已合并文件的缓存"""
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try:
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with open(self.cache_file, 'w', encoding='utf-8') as f:
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json.dump(self.merged_files, f, ensure_ascii=False, indent=2)
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logger.info(f"已更新合并文件缓存，共{len(self.merged_files)} 条记录")
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except Exception as e:
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logger.warning(f"保存缓存文件失败: {e}")
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def get_latest_purchase_orders(self):
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"""
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获取output目录下最新的采购单Excel文件
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"""
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logger.info(f"搜索目录 {self.output_dir} 中的采购单Excel文件")
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excel_files = []
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for file in os.listdir(self.output_dir):
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# 只处理以"采购单_"开头的Excel文件
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if file.lower().endswith('.xls') and file.startswith('采购单_'):
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file_path = os.path.join(self.output_dir, file)
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excel_files.append((file_path, os.path.getmtime(file_path)))
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if not excel_files:
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logger.warning(f"未在 {self.output_dir} 目录下找到采购单Excel文件")
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return []
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# 按修改时间排序，获取最新的文件
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sorted_files = sorted(excel_files, key=lambda x: x[1], reverse=True)
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logger.info(f"找到{len(sorted_files)} 个采购单Excel文件")
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return [file[0] for file in sorted_files]
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def read_purchase_order(self, file_path):
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"""
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读取采购单Excel文件
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"""
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try:
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# 读取Excel文件
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df = pd.read_excel(file_path)
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logger.info(f"成功读取采购单文件: {file_path}")
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# 打印列名，用于调试
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logger.info(f"Excel文件的列名: {df.columns.tolist()}")
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# 检查是否有特殊表头结构（如"武侯环球乐百惠便利店3333.xlsx"）
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# 判断依据：检查第3行是否包含常见的商品表头信息
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special_header = False
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if len(df) > 3: # 确保有足够的行
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row3 = df.iloc[3].astype(str)
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header_keywords = ['行号', '条形码', '条码', '商品名称', '规格', '单价', '数量', '金额', '单位']
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# 计算匹配的关键词数量
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matches = sum(1 for keyword in header_keywords if any(keyword in str(val) for val in row3.values))
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# 如果匹配了至少3个关键词，认为第3行是表头
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if matches >= 3:
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logger.info(f"检测到特殊表头结构，使用第3行作为列名: {row3.values.tolist()}")
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# 创建新的数据帧，使用第3行作为列名，数据从第4行开始
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header_row = df.iloc[3]
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data_rows = df.iloc[4:].reset_index(drop=True)
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# 为每一列分配一个名称（避免重复的列名）
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new_columns = []
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for i, col in enumerate(header_row):
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col_str = str(col)
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if col_str == 'nan' or col_str == 'None' or pd.isna(col):
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new_columns.append(f"Col_{i}")
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else:
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new_columns.append(col_str)
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# 使用新列名创建新的DataFrame
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data_rows.columns = new_columns
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df = data_rows
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special_header = True
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logger.info(f"重新构建的数据帧列名: {df.columns.tolist()}")
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# 定义可能的列名映射
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column_mapping = {
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'条码': ['条码', '条形码', '商品条码', 'barcode', '商品条形码', '条形码', '商品条码', '商品编码', '商品编号', '条形码', '条码（必填）'],
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'采购量': ['数量', '采购数量', '购买数量', '采购数量', '订单数量', '采购数量', '采购量（必填）'],
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'采购单价': ['单价', '价格', '采购单价', '销售价', '采购单价（必填）'],
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'赠送量': ['赠送量', '赠品数量', '赠送数量', '赠品']
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}
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# 映射实际的列名
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mapped_columns = {}
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for target_col, possible_names in column_mapping.items():
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for col in df.columns:
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# 移除列名中的空白字符和括号内容以进行比较
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clean_col = re.sub(r'\s+', '', str(col))
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clean_col = re.sub(r'（.*?）', '', clean_col) # 移除括号内容
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for name in possible_names:
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clean_name = re.sub(r'\s+', '', name)
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clean_name = re.sub(r'（.*?）', '', clean_name) # 移除括号内容
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if clean_col == clean_name:
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mapped_columns[target_col] = col
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break
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if target_col in mapped_columns:
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break
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# 如果找到了必要的列，重命名列
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if mapped_columns:
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df = df.rename(columns=mapped_columns)
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logger.info(f"列名映射结果: {mapped_columns}")
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return df
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except Exception as e:
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logger.error(f"读取采购单文件失败: {file_path}, 错误: {str(e)}")
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return None
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def merge_purchase_orders(self, file_paths):
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"""
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合并多个采购单文件
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"""
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if not file_paths:
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logger.warning("没有需要合并的采购单文件")
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return None
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# 读取所有采购单文件
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dfs = []
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for file_path in file_paths:
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df = self.read_purchase_order(file_path)
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if df is not None:
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# 确保条码列是字符串类型
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df['条码（必填）'] = df['条码（必填）'].astype(str)
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# 去除可能的小数点和.0
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df['条码（必填）'] = df['条码（必填）'].apply(lambda x: x.split('.')[0] if '.' in x else x)
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# 处理NaN值，将其转换为空字符串
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for col in df.columns:
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df[col] = df[col].apply(lambda x: '' if pd.isna(x) else x)
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dfs.append(df)
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if not dfs:
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logger.error("没有成功读取任何采购单文件")
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return None
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# 合并所有数据框
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merged_df = pd.concat(dfs, ignore_index=True)
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logger.info(f"合并了{len(dfs)} 个采购单文件，共{len(merged_df)} 条记录")
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# 检查并合并相同条码和单价的数据
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merged_data = {}
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for _, row in merged_df.iterrows():
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# 使用映射后的列名访问数据
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barcode = str(row['条码（必填）']) # 保持字符串格式
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# 移除条码中可能的小数点
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barcode = barcode.split('.')[0] if '.' in barcode else barcode
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unit_price = float(row['采购单价（必填）'])
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quantity = float(row['采购量（必填）'])
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# 检查赠送量是否为空
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has_gift = '赠送量' in row and row['赠送量'] != '' and not pd.isna(row['赠送量'])
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gift_quantity = float(row['赠送量']) if has_gift else ''
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# 商品名称处理，确保不会出现"nan"
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product_name = row['商品名称']
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if pd.isna(product_name) or product_name == 'nan' or product_name == 'None':
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product_name = ''
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# 创建唯一键：条码+单价
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key = f"{barcode}_{unit_price}"
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if key in merged_data:
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# 如果已存在相同条码和单价的数据，累加数量
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merged_data[key]['采购量（必填）'] += quantity
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# 如果当前记录有赠送量且之前的记录也有赠送量，则累加赠送量
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if has_gift and merged_data[key]['赠送量'] != '':
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merged_data[key]['赠送量'] += gift_quantity
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# 如果当前记录有赠送量但之前的记录没有，则设置赠送量
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elif has_gift:
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merged_data[key]['赠送量'] = gift_quantity
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# 其他情况保持原样（为空）
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logger.info(f"合并相同条码和单价的数据: 条码={barcode}, 单价={unit_price}, 数量={quantity}, 赠送量={gift_quantity}")
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# 如果当前商品名称不为空，且原来的为空，则更新商品名称
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if product_name and not merged_data[key]['商品名称']:
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merged_data[key]['商品名称'] = product_name
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else:
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# 如果是新数据，直接添加
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merged_data[key] = {
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'商品名称': product_name,
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'条码（必填）': barcode, # 使用处理后的条码
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'采购量（必填）': quantity,
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'赠送量': gift_quantity,
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'采购单价（必填）': unit_price
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}
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# 将合并后的数据转换回DataFrame
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final_df = pd.DataFrame(list(merged_data.values()))
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logger.info(f"合并后剩余{len(final_df)} 条唯一记录")
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return final_df
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def create_merged_purchase_order(self, df):
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"""
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创建合并后的采购单Excel文件
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"""
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try:
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# 获取当前时间戳
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timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
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# 创建输出文件路径
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output_file = os.path.join(self.output_dir, f"合并采购单_{timestamp}.xls")
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# 打开模板文件
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workbook = xlrd.open_workbook(self.template_path)
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workbook = xlcopy(workbook)
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worksheet = workbook.get_sheet(0)
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# 从第2行开始填充数据
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row_index = 1
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# 按条码排序
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df = df.sort_values('条码（必填）')
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# 填充数据
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for _, row in df.iterrows():
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# 1. 列A(0): 商品名称
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product_name = str(row['商品名称'])
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# 检查并处理nan值
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if product_name == 'nan' or product_name == 'None':
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product_name = ''
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worksheet.write(row_index, 0, product_name)
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# 2. 列B(1): 条码
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worksheet.write(row_index, 1, str(row['条码（必填）']))
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# 3. 列C(2): 采购量
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worksheet.write(row_index, 2, float(row['采购量（必填）']))
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# 4. 列D(3): 赠送量
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# 只有当赠送量不为空且不为0时才写入
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if '赠送量' in row and row['赠送量'] != '' and not pd.isna(row['赠送量']):
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# 将赠送量转换为数字
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try:
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gift_quantity = float(row['赠送量'])
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# 只有当赠送量大于0时才写入
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if gift_quantity > 0:
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worksheet.write(row_index, 3, gift_quantity)
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except (ValueError, TypeError):
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# 如果转换失败，忽略赠送量
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pass
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# 5. 列E(4): 采购单价
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style = xlwt.XFStyle()
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style.num_format_str = '0.0000'
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worksheet.write(row_index, 4, float(row['采购单价（必填）']), style)
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row_index += 1
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# 保存文件
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workbook.save(output_file)
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logger.info(f"合并采购单已保存: {output_file}")
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# 记录已合并文件
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for file_path in self.get_latest_purchase_orders():
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file_stat = os.stat(file_path)
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file_key = f"{os.path.basename(file_path)}_{file_stat.st_size}_{file_stat.st_mtime}"
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self.merged_files[file_key] = output_file
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self._save_merged_files()
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return output_file
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except Exception as e:
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logger.error(f"创建合并采购单失败: {str(e)}")
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return None
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def process(self):
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"""
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处理最新的采购单文件
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"""
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# 获取最新的采购单文件
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file_paths = self.get_latest_purchase_orders()
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if not file_paths:
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logger.error("未找到可处理的采购单文件")
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return False
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# 合并采购单
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merged_df = self.merge_purchase_orders(file_paths)
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if merged_df is None:
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logger.error("合并采购单失败")
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return False
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# 创建合并后的采购单
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output_file = self.create_merged_purchase_order(merged_df)
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if output_file is None:
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logger.error("创建合并采购单失败")
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return False
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logger.info(f"处理完成，合并采购单已保存至: {output_file}")
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return True
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def main():
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"""主程序"""
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import argparse
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# 解析命令行参数
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parser = argparse.ArgumentParser(description='合并采购单程序')
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parser.add_argument('--input', type=str, help='指定输入采购单文件路径，多个文件用逗号分隔')
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args = parser.parse_args()
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merger = PurchaseOrderMerger()
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# 处理采购单文件
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try:
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if args.input:
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# 使用指定文件处理
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file_paths = [path.strip() for path in args.input.split(',')]
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merged_df = merger.merge_purchase_orders(file_paths)
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if merged_df is not None:
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output_file = merger.create_merged_purchase_order(merged_df)
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if output_file:
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print(f"处理成功！合并采购单已保存至: {output_file}")
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else:
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print("处理失败！请查看日志了解详细信息")
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else:
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print("处理失败！请查看日志了解详细信息")
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else:
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# 使用默认处理流程（查找最新文件）
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result = merger.process()
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if result:
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print("处理成功！已将数据合并并保存")
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else:
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print("处理失败！请查看日志了解详细信息")
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except Exception as e:
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logger.error(f"处理过程中发生错误: {e}", exc_info=True)
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print(f"处理过程中发生错误: {e}")
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print("请查看日志文件了解详细信息")
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if __name__ == "__main__":
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try:
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main()
|
||
except Exception as e:
|
||
logger.error(f"程序执行过程中发生错误: {e}", exc_info=True)
|
||
sys.exit(1) |