在之前使用的几个版本的脚本中，发现由于读取的数据和index信息不一致导致写入的结果变少，故增加判断，在生成文件过程中方便直接发现异常，并进行调整输入的文件信息。
此版本是在之前的只做了修改，未进行优化：
后续优化：
txt文本可用pandas模块读取
本次的异常判断可用try except
后续再进行优化

#_*_coding:UTF-8_*_
import xlrd
import csv
import datetime
# import pandas as pd


def read_excel(lib_and_index_info):
    pooling_file = xlrd.open_workbook(r"*.xlsx", "r")
    table = pooling_file.sheet_by_name("Sheet1")
    n_rows = table.nrows  # 获取行数


    # lib_and_index_info = {}  # 新建字典存储编号以及index序列信息 键为实验室编号信息，值为index信息
    for i in range(5, n_rows):  # pooling表的第5行开始为实验室记录的文库及index取样等信息的表头
        lib_num = table.cell(i, 1).value
        index_info = table.cell(i, 3).value
        # 如果单元格中有空白行或者只有一个cell有数据的删除
        lib_and_index_info[lib_num] = index_info  # 生成样本编号与index对应关系的字典
        if index_info == "":  # 判断index中信息是否为空值
            lib_and_index_info.pop(lib_num)  # 删除值为空的键，即从字典中删除了该信息

def read_index_seq(index_dict):
    # index_dict = {}
    """
    增加了对index碱基数的if判断，在调用该脚本是写入使用的是8或者6碱基的模式，
    生成满足6碱基或者8碱基的index序列
    :param index_dict:
    :return: 
    """
    index_num = int(input("输入index的碱基数： "))  # 数值类型转换
    # index_num = input("输入index的碱基数： ")  # 该命令直接执行else后的语句
    with open(r"D:\My Projects\DoubleIndex.txt", "r") as index_file:
        if index_num == 8:
            for i in range(1, 96):
                lines = index_file.readline()
                index_name = lines.split("\t")[0]
                P7_seq = lines.split("\t")[2]
                P5_seq = lines.split("\t")[4]
                index_dict[index_name] = [P7_seq, P5_seq]
        else:
            for i in range(1, 96):
                lines = index_file.readline()
                index_name = lines.split("\t")[0]
                P7_seq = lines.split("\t")[2][:6]
                P5_seq = lines.split("\t")[4][:6]
                index_dict[index_name] = [P7_seq, P5_seq]
    index_file.close()


def write_csv(lib_and_index_info,index_dict):
    global write_sam_num  # 定义全局变量，用于统计写入的数量，并返回该结果用于后续判断
    write_sam_num = 0
    current_time = datetime.datetime.now().strftime('%Y/%m/%d')
    with open(r"D:\My Projects\temp.csv", "w", newline="") as temp_csv:  # newline=""控制行距，经测试只可在python3环境下使用
        csv_writer = csv.writer(temp_csv)
        csv_writer.writerow(["[Header]"])
        col_names = ["Sample_ID", "Sample_Name", "Sample_Plate", "Sample_Well",
                     "I7_Index_ID", "index", "index2","Sample_Project", "Description"]  # 表头信息
        csv_writer = csv.DictWriter(temp_csv, fieldnames=col_names)  # 添加表头信息，用于构建字典
        csv_writer.writeheader()
        for k_lib, v_lib in lib_and_index_info.items():
            for k_index, v_index in index_dict.items():
                if v_lib == k_index:
                    # index_P7 = v_index[0]
                    # index_P5 = v_index[1]
                    csv_writer.writerow(
                        {"Sample_ID": k_lib, "Sample_Name": k_lib, "I7_Index_ID": v_lib, "index": v_index[0],
                         "index2": v_index[1], "Sample_Project": "fastq", "Description": "fastq"})
                    write_sam_num += 1


        temp_csv.close()
        return write_sam_num


def main():  # 主函数
    lib_and_index_info = {}
    index_dict = {}
    read_excel(lib_and_index_info)
    read_index_seq(index_dict)
    write_csv(lib_and_index_info,index_dict)
    # 异常处理
    if write_sam_num == len(lib_and_index_info):  # 增加判断写入的数据和读取的数据个数是否一致
        print("Numbers of sample to be analyzed:{}".format(len(lib_and_index_info)))
    else:
        print("Please check the sample information of the output file：Inconsistent results")
if __name__ == "__main__":
    main()

记录于20191003