126 lines
4.7 KiB
Python
126 lines
4.7 KiB
Python
import zipfile,os,re
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import pandas as pd
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from pathlib import Path
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#设置工作目录, 要求工作目录中恰有一个.txt文件（或.tex文件)和一些.zip文件，其余不论
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# 第一行用"#"开头的作业数据不会被读取
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filepath = r"C:\Users\weiye\Documents\wwy sync\xiaoxian待导入"
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#设置届别与接受的比例阈值
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semester = 2023
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# threshold = 0.5 #当班级提交人数超过该比例时数据有效
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def getindex(string,pos = 2):
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para = string.split(".")
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return int(para[pos-1])
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def stringcount(string,list):
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theitem = ""
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count = 0
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for item in list:
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if string in item:
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count += 1
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theitem = item
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return (count,theitem)
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shiftdict = {"高一": 3, "高二": 2, "高三": 1}
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patterns = [r"填空([\d\.]+)\((\d+)\)",r"单选([\d\.]+)\((\d+)\)",r"^([\d\.]+)第\d+(步)"]
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#生成文件名tex_file和zip_file
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files = [os.path.join(filepath,f) for f in os.listdir(filepath)]
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tex_file = [f for f in files if ".tex" in f or ".txt" in f][0]
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zip_files = [f for f in files if ".zip" in f]
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#分割各次作业数据
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with open(tex_file,"r",encoding = "utf8") as f:
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tex_data = f.read().strip()
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tex_data = re.sub(r"\t+",r" ",tex_data)
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tex_data = re.sub(r"\n{2,}","---split---",tex_data)
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homeworklist = tex_data.split("---split---")
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#读取各次作业首行（文件名）与次行（日期）并组织字典结构
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homeworkdict = {}
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for hwk in homeworklist:
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hwkdata = hwk.strip().split("\n")
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id = hwkdata.pop(0).replace(" ","")
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date = hwkdata.pop(0)
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if not id.startswith("#"):
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homeworkdict[id] = {}
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homeworkdict[id]["date"] = date
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homeworkdict[id]["usage_data"] = hwkdata
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#测试是否每一项都有相应的zip文件与之对应
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execflag = True
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for id in homeworkdict:
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if stringcount(id,zip_files)[0] == 1:
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print("zip文件在文件夹中:",id)
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else:
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execflag = False
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print("!!!zip文件个数不对:",id)
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if execflag:
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outputstr = "usages\n\n"
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for hid in homeworkdict:
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print("正在处理%s"%id)
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date = homeworkdict[hid]["date"]
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#在zip文件中找到包含正确率数据的文件
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zip_file = os.path.join(filepath,stringcount(hid,zip_files)[1])
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zf = zipfile.ZipFile(zip_file)
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# statfiles = [f.filename for f in zf.filelist if "试题分析" in f.filename]
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handinfiles = [f.filename for f in zf.filelist if "小题分_按学号" in f.filename]
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if "statsfile.xlsx" in os.listdir("临时文件"):
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os.remove("临时文件/statsfile.xlsx")
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extractedpath = Path(zf.extract(handinfiles[0]))
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extractedpath.rename("临时文件/statsfile.xlsx")
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df = pd.read_excel("临时文件/statsfile.xlsx",skiprows=2)
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indices = {}
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for col in df.columns:
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for pattern in patterns:
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res = re.findall(pattern,col)
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if len(res) > 0:
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id,mark = res[0]
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if not id in indices:
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indices[id] = {}
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if not "步" in mark:
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indices[id][col] = int(mark)
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else:
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indices[id][col] = int(input(f"{hid}-{col}的满分:"))
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corresp_dict = {}
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homework = homeworkdict[hid]
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data = homework["date"]
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for rawline in homework["usage_data"]:
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line = re.sub(r"[\t\s]+"," ",rawline)
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a,b = line.split(" ")
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if a.strip() in indices:
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corresp_dict[b.strip()]=indices[a].copy()
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# print(corresp_dict)
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for excelfile in handinfiles:
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if "statsfile.xlsx" in os.listdir("临时文件"):
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os.remove("临时文件/statsfile.xlsx")
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extractedpath = Path(zf.extract(excelfile))
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extractedpath.rename("临时文件/statsfile.xlsx")
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df = pd.read_excel("临时文件/statsfile.xlsx",skiprows=2)[:-2]
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gradename = re.findall(r"高[一二三]",excelfile)[0]
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classname = str(semester+shiftdict[gradename])+"届"+gradename+re.findall(r"高[一二三]([\d]*?)班",excelfile)[0].zfill(2)+"班"
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for id in corresp_dict:
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colandmarks = corresp_dict[id]
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currentstring = f"{id}\n{date}\t{classname}"
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for col in colandmarks:
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mark = colandmarks[col]
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diff = df[col].mean()/mark
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currentstring += f"\t{diff:.3f}"
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currentstring += "\n\n"
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outputstr += currentstring
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with open("临时文件/自动转换结果.txt","w",encoding = "utf8") as f:
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f.write(outputstr)
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with open("文本文件/metadata.txt","w",encoding = "utf8") as f:
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f.write(outputstr)
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zf.close()
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