141 lines
5.3 KiB
Plaintext
141 lines
5.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2025届高一11班\n",
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"2025届高一11班 有效\n",
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"2025届高一12班\n",
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"2025届高一12班 有效\n",
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"2025届高一01班\n",
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"2025届高一01班 有效\n",
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"2025届高一02班\n",
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"2025届高一02班 有效\n",
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"2025届高一06班\n",
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"2025届高一06班 有效\n"
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]
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}
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],
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"source": [
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"import zipfile,os,re,time\n",
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"import pandas as pd\n",
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"from pathlib import Path\n",
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"\n",
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"#设置工作目录, 要求工作目录中恰有一个.tex文件和一个.zip文件，其余不论\n",
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"filepath = \"数据导入作业文件\"\n",
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"\n",
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"#生成文件名tex_file和zip_file\n",
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"files = [os.path.join(filepath,f) for f in os.listdir(filepath)]\n",
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"tex_file = [f for f in files if \".tex\" in f or \".txt\" in f][0]\n",
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"zip_file = [f for f in files if \".zip\" in f][0]\n",
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"\n",
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"#获得题号数据，保存在problems_list中\n",
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"with open(tex_file,\"r\",encoding = \"utf8\") as f:\n",
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" tex_data = f.read()\n",
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"problems_list = [re.sub(r\"\\s+\",\"\\t\",l.strip()) for l in tex_data.split(\"\\n\") if l.strip() != \"\"]\n",
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"date = problems_list.pop(0)\n",
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"\n",
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"\n",
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"#在zip文件中找到包含正确率数据的文件\n",
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"zf = zipfile.ZipFile(zip_file)\n",
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"statfiles = [f.filename for f in zf.filelist if \"试题分析\" in f.filename]\n",
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"handinfiles = [f.filename for f in zf.filelist if \"学生成绩\" in f.filename]\n",
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"\n",
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"#生成答题纸区域编号与题目ID的对应\n",
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"correspondence_dict = {}\n",
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"if \"statsfile.xlsx\" in os.listdir(\"临时文件\"):\n",
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" os.remove(\"临时文件/statsfile.xlsx\")\n",
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"extractedpath = Path(zf.extract(statfiles[0]))\n",
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"extractedpath.rename(\"临时文件/statsfile.xlsx\")\n",
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"df = pd.read_excel(\"临时文件/statsfile.xlsx\")\n",
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"problems_indexes = list(df[df.columns[0]][2:])\n",
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"\n",
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"for pind in problems_indexes:\n",
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" for p in problems_list:\n",
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" if len(re.findall(\"^\"+pind+\"\\t\",p))>0:\n",
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" correspondence_dict[pind] = p[p.index(\"\\t\")+1:]\n",
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"\n",
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"outputstr = \"usages\\n\\n\"\n",
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"\n",
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"for sf in statfiles:\n",
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" #读取文件生成区域列表与难度列表\n",
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" classname = \"2025届高一\"+re.findall(r\"高一([\\d]*?)班\",sf)[0].zfill(2)+\"班\"\n",
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" print(classname)\n",
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" if \"statsfile.xlsx\" in os.listdir(\"临时文件\"):\n",
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" os.remove(\"临时文件/statsfile.xlsx\")\n",
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" extractedpath = Path(zf.extract(sf))\n",
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" extractedpath.rename(\"临时文件/statsfile.xlsx\")\n",
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" df = pd.read_excel(\"临时文件/statsfile.xlsx\")\n",
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" if \"handinfile.xlsx\" in os.listdir(\"临时文件\"):\n",
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" os.remove(\"临时文件/handinfile.xlsx\")\n",
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" extractedpath = Path(zf.extract(sf[:sf.find(\"数学\")]+\"学生成绩.xlsx\"))\n",
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" extractedpath.rename(\"临时文件/handinfile.xlsx\")\n",
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" handindf = pd.read_excel(\"临时文件/handinfile.xlsx\")\n",
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" if str(handindf.iloc[int(len(handindf)*0.66)][\"Unnamed: 4\"]) != \"缺考\":\n",
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" difficulties = list(df[df.columns[9]][2:])\n",
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" problems_indexes = list(df[df.columns[0]][2:])\n",
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" #生成该班级题目ID对应难度列表组成的字典\n",
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" class_difficulty = {}\n",
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" for i in range(len(difficulties)):\n",
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" if problems_indexes[i] in correspondence_dict:\n",
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" if not correspondence_dict[problems_indexes[i]] in class_difficulty:\n",
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" class_difficulty[correspondence_dict[problems_indexes[i]]] = [difficulties[i]]\n",
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" else:\n",
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" class_difficulty[correspondence_dict[problems_indexes[i]]].append(difficulties[i])\n",
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" #添加到输出字符串中\n",
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" for id in class_difficulty:\n",
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" outputstr += id + \"\\n\" + date + \"\\t\" + classname + \"\\t\" + \"\\t\".join([(\"%.3f\" %float(v)) for v in class_difficulty[id]]) + \"\\n\\n\"\n",
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" print(classname, \"有效\")\n",
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" else:\n",
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" print(classname, \"无效\")\n",
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"\n",
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"with open(\"临时文件/自动转换结果.txt\",\"w\",encoding = \"utf8\") as f:\n",
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" f.write(outputstr)\n",
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"with open(\"../文本文件/metadata.txt\",\"w\",encoding = \"utf8\") as f:\n",
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" f.write(outputstr)\n",
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"zf.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "mathdept",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.11"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "42dd566da87765ddbe9b5c5b483063747fec4aacc5469ad554706e4b742e67b2"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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