import json,re,os,Levenshtein,fitz,time def GetDate(): #获得当前日期 currentdate = str(time.localtime().tm_year)+str(time.localtime().tm_mon).zfill(2)+str(time.localtime().tm_mday).zfill(2) return currentdate #返回当前日期yyyymmdd #读取存储json数据库相关(不限于题号数据库) def load_dict(filename): #根据filename读取json数据库并转化为python字典 with open(filename,"r",encoding = "u8") as f: adict = json.loads(f.read()) return adict #返回python字典 def save_dict(adict,filename): #将adict字典转化为json文件并保存至filename文件中 try: with open(filename,"w",encoding = "u8") as f: f.write(json.dumps(adict,indent=4,ensure_ascii=False)) return 0 #成功则返回0 except: return 1 #不成功则返回1 def pre_treating(string): #删除字符串中对比较无用的字符, 以供比较 string = re.sub(r"\\begin\{center\}[\s\S]*?\\end\{center\}","",string) string = re.sub(r"(bracket\{\d+\})|(blank\{\d+\})|(fourch)|(twoch)|(onech)","",string) string = re.sub(r"[\s\\\{\}\$\(\)\[\]]","",string) string = re.sub(r"[\n\t]","",string) string = re.sub(r"(displaystyle)|(overrightarrow)|(overline)","",string) string = re.sub(r"[,\.:;?]","",string) return string #返回处理后的字符串 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 treated_dict = {} for id in p_dict: treated_dict[id] = {} treated_dict[id]["content"] = pre_treating(p_dict[id]["content"]) treated_dict[id]["same"] = p_dict[id]["same"] return treated_dict #返回处理后的字典, 含内容字段及相同题目字段 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） maxsim = -1 argmaxsim = "000000" for id in adict: if not id in excludelist: simrate = Levenshtein.jaro(adict[id]["content"],adict[currentid]["content"]) if simrate > maxsim: maxsim = simrate argmaxsim = id return (maxsim,argmaxsim) #返回最大关联系数与关联程度最大的题号 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 excludelist = [startingid] currentid = startingid for i in range(length): maxsim,currentid = detectmaxsim(currentid,excludelist,adict) excludelist.append(currentid) return ",".join(excludelist) #返回按顺序的题号列表 def generate_number_set(string,*thedict): #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"] #可变参数*dict如果存在, 将只生成dict的keys中包含的题号列表 string = re.sub(r"[\n\s]","",string) string_list = string.split(",") numbers_list = [] for s in string_list: if not ":" in s: numbers_list.append(s.zfill(6)) else: start,end = s.split(":") for ind in range(int(start),int(end)+1): numbers_list.append(str(ind).zfill(6)) if len(thedict) == 0: return numbers_list #返回六位题号列表 elif len(thedict) == 1 and type(thedict[0]) == dict: numbers_list = [id for id in numbers_list if id in thedict[0]] return numbers_list #返回字典中存在的六位题号列表 else: return "输入参数有误" 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" if not len(id_list) == 0: exp_list = [] start = id_list[0] current = start end = start for id in id_list[1:]: # print(id,current) if int(id)-1 == int(current): current = id end = id else: if not start == end: exp_list.append('"'+start+":"+end+'"') else: exp_list.append('"'+start+'"') start = id current = id end = id if not start == end: exp_list.append('"'+start+":"+end+'"') else: exp_list.append('"'+start+'"') exp_str = ",".join(exp_list).replace('"',"") else: exp_str = "无有效题号" return exp_str #返回含有":"或","的题号字符串 def parsePDF(filePath): #提取pdf文件中的字符 with fitz.open(filePath) as doc: text = "" for page in doc.pages(): text += page.get_text() + "\n" return text def extractIDs(filePath): #提取.txt,.tex或.pdf文件中的题号, 返回含有":"或","的题号字符串 if filePath[-4:] == ".txt" or filePath[-4:] == ".tex": with open(filePath,"r",encoding = "u8") as f: data = f.read() elif filePath[-4:] == ".pdf": data = parsePDF(filePath) else: return "格式不正确" ids = re.findall(r"\((\d{6})\)",data) return generate_exp(ids) def spareIDs(dictname): #返回空闲题号 idlist = list(dictname.keys()) used_str = generate_exp(idlist) used_list = used_str.split(",") output = "" for group in range(len(used_list)-1): output += "首个空闲id: %s, 直至: %s"%(str(int(used_list[group][-6:])+1).zfill(6),str(int(used_list[group+1][:6])-1).zfill(6)) + "\n" output += "首个空闲id: %s, 直至: %s"%(str(int(used_list[-1][-6:])+1).zfill(6),"999999") return output #返回的是一个多行的字符串, 每一行中含有一个空闲题号的闭区间 def parse_usage(datastring): #对单个usages中的项的结果进行分词 datastring = re.sub(r"\s+","\t",datastring.strip()) datalist = datastring.split("\t") date = "" classname = "" diff = [] for item in datalist: if not "." in item and not "高" in item and not "班" in item: date = item elif "高" in item or "班" in item: classname = item else: diff.append(item) return({"date":date,"classname":classname,"difficulty":diff}) #返回一个字典, "date"表示日期, "classname"表示班级, "difficultiy"表示难度列表 def GenerateProblemListFromString(data): #从来自.tex文件的字符串生成题目列表, 每个item是一道题目, 新一行的%用作前缀 try: data = re.findall(r"\\begin\{document\}([\s\S]*?)\\end\{document\}",data)[0] except: pass data = re.sub(r"\n{2,}","\n",data) data = re.sub(r"\\item",r"\\enditem\\item",data) data = re.sub(r"\\end\{enumerate\}",r"\\enditem",data) #切除无关信息, 保留关键信息 problempositions = [] for item in re.finditer(r"\\item([\s\S]*?)\\enditem",data): problempositions.append(item.regs[1]) #确定题目内容所在位置 problem_list = [] for pos in problempositions: content = data[pos[0]:pos[1]].strip() content = re.sub(r"\n\%[\s\S]*$","",content) #题目内容 subdata = data[:pos[0]] #开始寻找出处中缀 suflist = re.findall(r"\n(\%\s{0,}[\S]+)\n",subdata) if len(suflist) == 0: suffix = "" else: suffix = suflist[-1].replace("%","").strip() problem_list.append((content,suffix)) return problem_list #返回一个列表, 每一项是一个由 题目内容 和 题目来源前缀 组成的元组 def CreateEmptyProblem(problem): # 根据已有的题目创建新的空题目 NewProblem = problem.copy() for field in NewProblem: if type(NewProblem[field]) == str: NewProblem[field] = "" elif type(NewProblem[field]) == list: NewProblem[field] = [] elif type(NewProblem[field]) == int or type(NewProblem[field]) == float: NewProblem[field] = -1 return NewProblem #返回一个空题目的字典, ID和内容待赋值 # 创建新题目 def CreateNewProblem(id,content,origin,dict,editor): # 构建一道新题目的字典 NewProblem = CreateEmptyProblem(dict["000001"]) NewProblem["id"] = str(id).zfill(6) NewProblem["content"] = content NewProblem["origin"] = origin NewProblem["edit"] = [editor] return NewProblem # 返回一道新题目的字典, 已赋新的ID, 内容, 来源和编辑者 def CreateIDLinks(old_id_list,new_id_list,*thedict): #建立已有id和新id之间的联系, thedict为可选, 选中的话即为当前字典, 会从new_id_list中排除当前字典中有的项 if len(thedict) == 1 and type(thedict[0]) == dict: new_id_list = [id for id in new_id_list if not id in thedict[0]] if len(old_id_list)>len(new_id_list): return "新ID个数不足." else: id_links = [] for i in range(len(old_id_list)): id_links.append((old_id_list[i],new_id_list[i])) return id_links # 返回id联系, 每个元组表示一对id, 前者是旧id, 后者是新id def CreateRelatedProblems(links,thedict,filepath): # 根据links关联生成待编辑的新题目字典, 等待编辑修改 try: new_dict = {} for item in links: old_id,new_id = item new_dict[old_id] = thedict[old_id].copy() new_dict[old_id]["id"] = new_id + "待替换" new_dict[old_id]["content"] = "(待编辑)" + new_dict[old_id]["content"] new_dict[old_id]["usages"] = [] new_dict[old_id]["same"] = [] new_dict[old_id]["unrelated"] = [] new_dict[old_id]["edit"] = new_dict[old_id]["edit"].copy() + [GetDate()+"\t"] new_dict[old_id]["origin"] += "-" + GetDate() + "修改" save_dict(new_dict,filepath) except: return 1 #异常返回1 return 0 #正常返回0 def ImportRelatedProblems(new_json,main_json): # 导入编辑过的关联题目json文件到主数据库 pro_dict = load_dict(main_json) new_dict = load_dict(new_json) for id in new_dict: new_id = new_dict[id]["id"].replace("待替换","") #新题号后需要跟"待替换"字样 if new_id in pro_dict: print("题号有重复") return 1 else: pro_dict[new_id] = new_dict[id].copy() pro_dict[new_id]["id"] = new_id pro_dict[id]["related"] += [new_id] pro_dict[new_id]["related"] += [id] print("导入关联题目 %s -> %s 信息成功."%(id,new_id)) save_dict(dict(sorted(pro_dict.items())),main_json) #保存至目标pro_dict文件 return 0 #正常返回0 def strip_suffix(originalString, suf_words_list): # 字符串去除指定后缀 for sw in suf_words_list: output = re.sub(sw+r"[\S]*$","",originalString) return(output) # 返回原字符串中截去suf_words_list及之后字符的部分 def get_striped_origin(pro_dict,id,suf_words_list): # 题目来源去除指定后缀 return strip_suffix(pro_dict[id]["origin"],suf_words_list) # 返回去除指定后缀后的题目来源 if __name__ == "__main__": print("数据库工具, import用.")