105 lines
4.3 KiB
Python
105 lines
4.3 KiB
Python
import json,re,os,Levenshtein,fitz
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#读取存储json数据库相关(不限于题号数据库)
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def load_dict(filename): #根据filename读取json数据库并转化为python字典
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with open(filename,"r",encoding = "u8") as f:
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adict = json.loads(f.read())
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return adict #返回python字典
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def save_dict(adict,filename): #将adict字典转化为json文件并保存至filename文件中
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try:
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with open(filename,"w",encoding = "u8") as f:
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f.write(json.dumps(adict,indent=4,ensure_ascii=False))
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return 0 #成功则返回0
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except:
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return 1 #不成功则返回1
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def pre_treating(string): #删除字符串中对比较无用的字符, 以供比较
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string = re.sub(r"\\begin\{center\}[\s\S]*?\\end\{center\}","",string)
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string = re.sub(r"(bracket\{\d+\})|(blank\{\d+\})|(fourch)|(twoch)|(onech)","",string)
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string = re.sub(r"[\s\\\{\}\$\(\)\[\]]","",string)
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string = re.sub(r"[\n\t]","",string)
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string = re.sub(r"(displaystyle)|(overrightarrow)|(overline)","",string)
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string = re.sub(r"[,\.:;?]","",string)
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return string #返回处理后的字符串
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def treat_dict(p_dict): #对整个题库字典中的内容部分进行预处理，删除无用字符
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treated_dict = {}
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for id in p_dict:
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treated_dict[id] = {}
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treated_dict[id]["content"] = pre_treating(p_dict[id]["content"])
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treated_dict[id]["same"] = p_dict[id]["same"]
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return treated_dict #返回处理后的字典, 含内容字段及相同题目字段
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def detectmaxsim(currentid,excludelist,adict): #检测与已知题目关联程度最大的题目（除外列表之外的部分）
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maxsim = -1
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argmaxsim = "000000"
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for id in adict:
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if not id in excludelist:
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simrate = Levenshtein.jaro(adict[id]["content"],adict[currentid]["content"])
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if simrate > maxsim:
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maxsim = simrate
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argmaxsim = id
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return (maxsim,argmaxsim) #返回最大关联系数与关联程度最大的题号
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def generate_problem_series(startingid,length,adict): #在adict字典里返回从startingid开始的一系列题号, 每一题都是与上一题的关联程度最大的
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excludelist = [startingid]
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currentid = startingid
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for i in range(length):
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maxsim,currentid = detectmaxsim(currentid,excludelist,adict)
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excludelist.append(currentid)
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return ",".join(excludelist) #返回按顺序的题号列表
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def generate_number_set(string): #根据可能含有":"和","的题号字符串生成一个用逗号分隔的六位题号列表, 例如"1:3,5"会生成["000001","000002","000003","000005"]
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string = re.sub(r"[\n\s]","",string)
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string_list = string.split(",")
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numbers_list = []
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for s in string_list:
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if not ":" in s:
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numbers_list.append(s.zfill(6))
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else:
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start,end = s.split(":")
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for ind in range(int(start),int(end)+1):
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numbers_list.append(str(ind).zfill(6))
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return numbers_list #返回六位题号列表
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def generate_exp(id_list): #根据题号列表生成字符串式的含":"和","的题号字符串, 例如["000001","000002","000003","000005"]生成"000001:000003,000005", 若列表为空则生成"无有效题号"
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if not len(id_list) == 0:
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exp_list = []
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start = id_list[0]
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current = start
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end = start
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for id in id_list[1:]:
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# print(id,current)
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if int(id)-1 == int(current):
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current = id
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end = id
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else:
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if not start == end:
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exp_list.append('"'+start+":"+end+'"')
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else:
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exp_list.append('"'+start+'"')
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start = id
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current = id
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end = id
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if not start == end:
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exp_list.append('"'+start+":"+end+'"')
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else:
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exp_list.append('"'+start+'"')
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exp_str = ",".join(exp_list).replace('"',"")
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else:
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exp_str = "无有效题号"
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return exp_str #返回含有":"或","的题号字符串
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def parsePDF(filePath): #提取pdf文件中的字符
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with fitz.open(filePath) as doc:
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text = ""
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for page in doc.pages():
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text += page.get_text() + "\n"
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return text
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if __name__ == "__main__":
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print("数据库工具, import用.") |