189 lines
7.3 KiB
Python
189 lines
7.3 KiB
Python
import json
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import os
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import unicodedata
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from collections import OrderedDict
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from pprint import pprint
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'''
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========== FileStructureTransfer ==========
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1. 本程序用于将获取的数据进行结构调整
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2. 根据论文发表的时间年限，分别将最后的数据存储在四个 json 文件中
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（1） newest： 发表于 2020 年之后
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（2） oldest： 发表于 2010 年之前
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（3） 2010-2014： 发表于 2010 年至 2014 年
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（4） 2015-2020： 发表于 2015 年至 2020 年
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3. 考虑到部分网站的总数据量过大，所以分成多份
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4. 本程序运行顺序为：
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（1） fileReader() 读取本地已爬取数据，存入待处理列表
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（2） arDataTransform() 转换论文数据格式
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（3） auDataTransform() 转换作者数据格式
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（4） 存入转换后数据的存储文件夹
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'''
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# Read the data
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def fileReader(folder, dataset):
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files = os.listdir(folder)
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for file in files:
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file_path = os.path.join(folder, file)
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with open(file_path, 'r', encoding='utf-8') as json_file:
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Data = json.load(json_file)
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dataset.append(Data)
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return dataset
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# Article data structure transfer
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def arDataTransform(au_folder, ar_dataset, num):
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def auInfoFind(path, file_name, ar_data, num):
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authors = ar_data.get('authors')
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authors.extend(ar_data.get('corresponding_authors'))
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file_path = os.path.join(path, file_name)
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with open(file_path, 'r', encoding='utf-8') as file:
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Data = json.load(file)
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au_ID = [] # A new list to store author_id
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# Find the author_id
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for author in authors:
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for Dict in Data:
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Dict_name = Dict.get('first_name') + ' ' + Dict.get('last_name')
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Dict_name = ''.join(char for char in unicodedata.normalize('NFKD', Dict_name) if
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unicodedata.category(char) != 'Mn')
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if Dict.get('from_article') == ar_data.get('article_id') and Dict_name == author:
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au_ID.append(Dict.get('author_id'))
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# Change the structure
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ar_data_transform = {
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"article_id": ar_data['article_id'],
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"title": ar_data['title'],
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"authors": au_ID,
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"authors_name": authors,
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"submit_datetime": ar_data['submit_datetime'],
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"publish_datetime": ar_data['publish_datetime'],
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"keywords": ar_data['keywords'],
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"MSC": ar_data['MSC'],
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"URL": ar_data['URL'],
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"DOI": ar_data['DOI'],
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"publisher": ar_data['publisher'],
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"journal": ar_data['journal'],
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"volume": ar_data['volume'],
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"issue": ar_data['issue'],
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"page": ar_data['page']
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}
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num[0] += 1 # Update the counter
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return ar_data_transform
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# ====== Main code for function =====
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ar_names = os.listdir(au_folder) # Read the folder
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for ar_list in ar_dataset:
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for Dict in ar_list:
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if num[0] % 100 == 0 and num[0] != 0: # Alert for complete data
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print(str(num[0]) + " copies of article data structure have been transformed.")
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if int(Dict.get('volume')) <= 2009:
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Dict = auInfoFind(au_folder, ar_names[3], Dict, num)
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ar_dataset_new[3].append(Dict)
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elif 2010 <= int(Dict.get('volume')) <= 2014:
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Dict = auInfoFind(au_folder, ar_names[0], Dict, num)
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ar_dataset_new[0].append(Dict)
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elif 2015 <= int(Dict.get('volume')) <= 2020:
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Dict = auInfoFind(au_folder, ar_names[1], Dict, num)
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ar_dataset_new[1].append(Dict)
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else:
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Dict = auInfoFind(au_folder, ar_names[2], Dict, num)
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ar_dataset_new[2].append(Dict)
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# Store into the new file
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filepaths = [
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"./SpringerOpen_buffer_transform/Article_output/SpringerOpen_Article_output_file(2010-2014).json",
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"./SpringerOpen_buffer_transform/Article_output/SpringerOpen_Article_output_file(2015-2020).json",
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"./SpringerOpen_buffer_transform/Article_output/SpringerOpen_Article_output_file(newest).json",
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"./SpringerOpen_buffer_transform/Article_output/SpringerOpen_Article_output_file(oldest).json",
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]
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for i in range(4):
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with open(filepaths[i], 'w', encoding='utf-8') as json_file:
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json.dump(ar_dataset_new[i], json_file, indent=4)
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print("\nComplete: All of the article data structure have been transformed.")
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# Author data structure transfer
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def auDataTransform(au_dataset, num):
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def transform(list, num):
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new_list = [] # New list to store transformed data
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for au_data in list:
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if num[0] % 100 == 0 and num[0] != 0: # Alert for complete data
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print(str(num[0]) + " copies of author data structure have been transformed.\n")
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if au_data['middle_name'] is not None:
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raw_name = au_data['first_name'] + ' ' + au_data['middle_name'] + ' ' + au_data['last_name']
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else:
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raw_name = au_data['first_name'] + ' ' + au_data['last_name']
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au_data_transform = {
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"author_id": au_data['author_id'],
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"from_article": au_data['from_article'],
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"first_name": au_data['first_name'],
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"last_name": au_data['last_name'],
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"middle_name": au_data['middle_name'],
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"raw_name": raw_name,
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"affiliation": au_data['affiliation']
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}
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new_list.append(au_data_transform)
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num[0] += 1 # Update the counter
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return new_list
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for i in range(4):
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au_list = transform(au_dataset[i], num)
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au_dataset_new[i].extend(au_list)
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# Store into the new file
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filepaths = [
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"./SpringerOpen_buffer_transform/Author_output/SpringerOpen_Author_output_file(2010-2014).json",
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"./SpringerOpen_buffer_transform/Author_output/SpringerOpen_Author_output_file(2015-2020).json",
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"./SpringerOpen_buffer_transform/Author_output/SpringerOpen_Author_output_file(newest).json",
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"./SpringerOpen_buffer_transform/Author_output/SpringerOpen_Author_output_file(oldest).json",
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]
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for i in range(4):
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with open(filepaths[i], 'w', encoding='utf-8') as json_file:
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json.dump(au_dataset_new[i], json_file, indent=4)
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print("\nComplete: All of the author data structure have been transformed.")
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# ========== Main code ========== #
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# New list for storing data
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ar_dataset = []
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au_dataset = []
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ar_dataset_new = [[] for _ in range(4)] # New list for transformed data
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au_dataset_new = [[] for _ in range(4)] # New list to store transformed data
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num1 = [0] # Counter for complete ar_date
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num2 = [0] # Counter for complete au_data
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os.makedirs('./SpringerOpen_buffer_transform/Article_output/', exist_ok=True)
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os.makedirs('./SpringerOpen_buffer_transform/Author_output/', exist_ok=True)
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# Read the data
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ar_dataset = fileReader('./SpringerOpen_buffer/Article_output', ar_dataset)
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au_dataset = fileReader('./SpringerOpen_buffer/Author_output', au_dataset)
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# Change the structure
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# arDataTransform('./SpringerOpen_buffer/Author_output', ar_dataset, num1)
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auDataTransform(au_dataset, num2) |