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Commit 424fd874 by jiangdongchen
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stage 1

parent 2c426667
Showing with 0 additions and 652 deletions
psrc/stage2/3-find_celebrity_from_paper_with_authors.py deleted 100644 → 0
from utils import contains_chinese, chinese_to_english_name, firstlast2lastfirst, name_in_niuren_list
import json
import pandas as pd
niuren_df = pd.read_csv("new_niuren_format-merged_turing.csv", encoding='utf-8-sig')
niuren_title_names = niuren_df.columns[2:].tolist()
niuren_name = pd.read_csv("new_niuren_format-merged_turing.csv", encoding='utf-8-sig')["name"].tolist()
niuren_name = [name.replace("\xa0", " ") for name in niuren_name] # 去除空格
# 打开 JSON Lines 文件并逐行读取
# file_path = 'allpapers_authors.jsonl' # 替换为你的文件路径
# file_path = '任务三pdf提取作者信息.jsonl' # 替换为你的文件路径
# file_path = 'good_papers-威.jsonl' # 替换为你的文件路径
file_path = 'good_papers.jsonl' # 替换为你的文件路径
# 创建一个空列表来存储解析后的 JSON 对象
data = []
# breakpoint()
# 逐行读取文件
with open(file_path, 'r', encoding='utf-8') as file:
for line in file:
# 解析每一行的 JSON 数据
json_line = json.loads(line)
data.append(json_line)
papers_with_niuren = []
for item in data:
item_insert_flag = False
authors = item['authors']
authors_name_list = [author['name'] for author in authors]
# breakpoint()
authors_name_list = [item if isinstance(item, str) else "" for item in authors_name_list] # 8788行为空nan
authors_name_list = [item if not contains_chinese(item) else chinese_to_english_name(item) for item in authors_name_list]
authors_name_list = [firstlast2lastfirst(item) for item in authors_name_list]
# print(authors_name_list)
item_niuren_authors = []
niuren_title_names_dict = {niuren_title: [] for niuren_title in niuren_title_names}
niuren_idx = 0
for idx, author_name in enumerate(authors_name_list):
# if author_name in niuren_name:
name_index = name_in_niuren_list(author_name, niuren_name)
if name_index != -1:
niuren_idx += 1
# print("找到匹配的作者：", author_name)
if not item_insert_flag:
item_insert_flag = True
# item_niuren_authors.append(author_name)
item_niuren_authors.append(idx + 1)
author_title = niuren_df.iloc[name_index][2:].tolist()
assert len(author_title) == len(niuren_title_names)
for author_title_name, author_title_value in zip(niuren_title_names, author_title):
if author_title_value == 1.0:
niuren_title_names_dict[author_title_name].append(niuren_idx)
item['niuren_authors'] = item_niuren_authors
for k, v in niuren_title_names_dict.items():
item[k] = "; ".join([str(i) for i in v])
# if item_insert_flag:
# papers_with_niuren.append(item)
# else:
# papers_with_niuren.append("empty line")
papers_with_niuren.append(item)
# save papers_with_niuren to jsonl file
# with open('good_papers-威-final.jsonl', 'w', encoding='utf-8') as f:
with open('good_papers-niuren.jsonl', 'w', encoding='utf-8') as f:
# with open('任务三pdf提取作者信息-final.jsonl', 'w', encoding='utf-8') as f:
for item in papers_with_niuren:
json.dump(item, f, ensure_ascii=False)
f.write('\n')
psrc/stage2/4-papers_with_niuren-final-to_csv.py deleted 100644 → 0
niuren_titles = {
"ACM Fellow": "",
"IEEE Fellow": "",
"AAAS": "",
"NAS ": "",
"NAE": "",
"NAI": "12",
"American Academy of Arts and Sciences": "",
"European Academy of Sciences": "",
"European Academy of Sciences and Arts": "",
"Academia Europaea (AE)": "",
"中国科学院": "",
"中国工程院": "",
"The Royal Society": "",
"The Royal Society of Canada": "",
"Turing Award": ""
}
from utils import read_jsonl
from copy import deepcopy
papers_with_niuren_final = read_jsonl("good_papers-niuren.jsonl")
# keys: 'title', 'authors', 'filename', 'niuren_authors'
# breakpoint()
import pandas as pd
df_data = {'title': [], 'authors': [], 'affiliations': [], 'filename': [], 'niuren_authors': []}
for key in niuren_titles.keys():
if key not in df_data.keys():
df_data[key] = []
for item in papers_with_niuren_final:
# if item == "empty line":
# continue
if "title" not in item.keys():
# continue
item["title"] = item["applicant"] # 如果是applicant不是title，就用applicant代替title
# 同时applicant情况下，author大概率没有affiliations，赋值[]
for author in item['authors']:
if "affiliations" not in author.keys():
author['affiliations'] = []
df_data['title'].append(item['title'])
author_names = []
author_affiliations = []
for author in item['authors']:
author_name = author['name']
author_names.append(author_name)
author_affiliations.extend(author['affiliations'])
niuren_authors = [author_names[idx - 1] for idx in item['niuren_authors']]
# author_names = list(set(author_names)) # 去重
author_affiliations = list(set(author_affiliations)) # 去重
df_data['authors'].append(deepcopy("; ".join(author_names)))
df_data['affiliations'].append(deepcopy("; ".join(author_affiliations)))
df_data['filename'].append(deepcopy(item['filename']))
df_data['niuren_authors'].append(deepcopy("; ".join(niuren_authors)))
for key in niuren_titles.keys():
df_data[key].append(deepcopy(item[key]))
# save to csv file
df = pd.DataFrame(df_data)
df.to_csv("good_papers-niuren.csv", index=False, encoding='utf-8-sig')
\ No newline at end of file
psrc/stage2/5-find_location_of_papers_with_niuren.py deleted 100644 → 0
from copy import deepcopy
from utils import get_continent
import pandas as pd
df = pd.read_excel("机构国家汇总-初版.xlsx")
# 提取第一行的值作为新的列名
new_columns = df.iloc[0].values
# 将第一行的值赋值给 df.columns
df.columns = new_columns
# 删除第一行（因为它已经被用作列名）
df = df.drop(0).reset_index(drop=True)
institution_name = df["Institution Name"].tolist()
file_name = "good_papers-niuren.csv"
final = pd.read_csv(file_name, encoding='utf-8-sig')
# add continent_country column
final["continent_country"] = ""
for row_idx, row in final.iterrows():
# if row["affiliations"] == "":
# continue
continent_country_row = []
# print(row_idx, row["affiliations"])
# if row["affiliations"] == "":
# continue
try:
aff_split = row["affiliations"].split(";")
except Exception as e:
print("没有机构信息：", row["affiliations"], "type:", type(row["affiliations"]))
continent_country_row = []
aff_split = []
aff_split = [x.strip() for x in aff_split]
for aff in aff_split:
# each aff
aff_in_flag = False
aff_split_2 = aff.split(",")
aff_split_2 = [x.strip() for x in aff_split_2]
row_index = -1
for x in aff_split_2:
if x in institution_name:
row_index = institution_name.index(x)
break
if row_index != -1:
country = df.iloc[row_index]["Country"]
continent = get_continent(country)
continent_country_row.append(f"{continent}-{country}")
continent_country_row = list(set(continent_country_row)) # 去重
continent_country_row = "; ".join(continent_country_row)
final.at[row_idx, "continent_country"] = deepcopy(continent_country_row)
# save it as an csv file
final.to_csv("good_papers-niuren-task_10.csv", index=False, encoding='utf-8-sig')
\ No newline at end of file
psrc/stage2/6-zhimingqiye.py deleted 100644 → 0
from copy import deepcopy
from utils import get_continent
import pandas as pd
df = pd.read_excel("知名企业.xlsx")
qiye_name = df["企业-英文名"].tolist()
file_name = "good_papers-niuren-task_10.csv"
final = pd.read_csv(file_name, encoding='utf-8-sig')
# add continent_country column
final["知名企业"] = ""
for row_idx, row in final.iterrows():
row_qiye = []
for qiye in qiye_name:
try:
if qiye in row["affiliations"]:
if qiye == "Intel":
# 排除 "Intel" 这个词在 "Intelligence" 和 "Intelligent" 中的情况
aff_str = deepcopy(row["affiliations"])
aff_str = aff_str.replace("Intelligence", "")
aff_str = aff_str.replace("Intelligent", "")
if qiye in aff_str:
row_qiye.append(deepcopy(qiye))
else:
row_qiye.append(deepcopy(qiye))
except Exception as e:
continue
row_qiye = list(set(row_qiye)) # 去重,可能也不需要去重
row_qiye = "; ".join(row_qiye)
final.at[row_idx, "知名企业"] = deepcopy(row_qiye)
# save it as an csv file
final.to_csv("good_papers-niuren-task_11.csv", index=False, encoding='utf-8-sig')
\ No newline at end of file
psrc/stage2/8-add_country_hpca.py deleted 100644 → 0
from utils import get_continent
import pandas as pd
from copy import deepcopy
hpca = pd.read_excel("hpca.xlsx")
new_hpca_columns = [
"届数",
"论文名称",
"第一作者",
"第一作者机构",
"第一作者洲-国家",
"通讯作者",
"通讯作者机构",
"通讯作者洲-国家",
]
new_hpca = pd.DataFrame(columns=new_hpca_columns)
new_hpca["届数"] = hpca["届数"]
new_hpca["论文名称"] = hpca["论文名称"]
new_hpca["第一作者"] = hpca["第一作者"]
new_hpca["第一作者机构"] = hpca["第一作者机构"]
# new_hpca["第一作者洲-国家"] = hpca["第一作者洲-国家"]
new_hpca["通讯作者"] = hpca["通讯作者"]
new_hpca["通讯作者机构"] = hpca["通讯作者机构"]
# new_hpca["通讯作者洲-国家"] = hpca["通讯作者洲-国家"]
df = pd.read_excel("机构国家汇总-初版.xlsx")
# 提取第一行的值作为新的列名
new_columns = df.iloc[0].values
# 将第一行的值赋值给 df.columns
df.columns = new_columns
# 删除第一行（因为它已经被用作列名）
df = df.drop(0).reset_index(drop=True)
institution_name = df["Institution Name"].tolist()
country_name = df["Country"].tolist()
for row_idx, row in new_hpca.iterrows():
diyi_flag = False
tongxun_flag = False
diyi_continent_country_row = []
tongxun_continent_country_row = []
try:
diyi_aff = row["第一作者机构"].replace("\xa0", " ").split(",")
assert ";" not in row["第一作者机构"]
except Exception as e:
# print("没有机构信息：", row["第一作者机构"], "type:", type(row["第一作者机构"]))
diyi_aff = []
try:
tongxun_aff = row["通讯作者机构"].replace("\xa0", " ").split(",")
assert ";" not in row["通讯作者机构"]
except Exception as e:
# print("没有机构信息：", row["通讯作者机构"], "type:", type(row["通讯作者机构"]))
tongxun_aff = []
diyi_aff = [x.strip() for x in diyi_aff]
diyi_aff_more_detail = []
for aff in diyi_aff:
diyi_aff_more_detail.extend(aff.split(" "))
diyi_aff_more_detail = [x.strip() for x in diyi_aff_more_detail]
diyi_aff_more_detail.extend(diyi_aff)
tongxun_aff = [x.strip() for x in tongxun_aff]
tongxun_aff_more_detail = []
for aff in tongxun_aff:
tongxun_aff_more_detail.extend(aff.split(" "))
tongxun_aff_more_detail = [x.strip() for x in tongxun_aff_more_detail]
tongxun_aff_more_detail.extend(tongxun_aff)
for aff in diyi_aff:
# each aff
if aff in institution_name:
diyi_flag = True
diyi_row_index = institution_name.index(aff)
country = df.iloc[diyi_row_index]["Country"]
continent = get_continent(country)
diyi_continent_country_row.append(f"{continent}-{country}")
if aff in country_name:
diyi_flag = True
diyi_row_index = country_name.index(aff)
continent = get_continent(aff)
diyi_continent_country_row.append(f"{continent}-{aff}")
for aff in diyi_aff_more_detail:
# each aff
if aff in institution_name:
diyi_flag = True
diyi_row_index = institution_name.index(aff)
country = df.iloc[diyi_row_index]["Country"]
continent = get_continent(country)
diyi_continent_country_row.append(f"{continent}-{country}")
if aff in country_name:
diyi_flag = True
diyi_row_index = country_name.index(aff)
continent = get_continent(aff)
diyi_continent_country_row.append(f"{continent}-{aff}")
# if diyi_flag == False:
# diyi_continent_country_row.append("")
for aff in tongxun_aff:
# each aff
if aff in institution_name:
tongxun_flag = True
tongxun_row_index = institution_name.index(aff)
country = df.iloc[tongxun_row_index]["Country"]
continent = get_continent(country)
tongxun_continent_country_row.append(f"{continent}-{country}")
if aff in country_name:
tongxun_flag = True
tongxun_row_index = country_name.index(aff)
continent = get_continent(aff)
tongxun_continent_country_row.append(f"{continent}-{aff}")
for aff in tongxun_aff_more_detail:
# each aff
if aff in institution_name:
tongxun_flag = True
tongxun_row_index = institution_name.index(aff)
country = df.iloc[tongxun_row_index]["Country"]
continent = get_continent(country)
tongxun_continent_country_row.append(f"{continent}-{country}")
if aff in country_name:
tongxun_flag = True
tongxun_row_index = country_name.index(aff)
continent = get_continent(aff)
tongxun_continent_country_row.append(f"{continent}-{aff}")
# if tongxun_flag == False:
# tongxun_continent_country_row.append("")
diyi_continent_country_row = list(set(diyi_continent_country_row)) # 去重
diyi_continent_country_row = "; ".join(diyi_continent_country_row)
new_hpca.at[row_idx, "第一作者洲-国家"] = deepcopy(diyi_continent_country_row)
tongxun_continent_country_row = list(set(tongxun_continent_country_row)) # 去重
tongxun_continent_country_row = "; ".join(tongxun_continent_country_row)
new_hpca.at[row_idx, "通讯作者洲-国家"] = deepcopy(tongxun_continent_country_row)
# save it as an csv file
new_hpca.to_csv("hpca-洲-国家.csv", index=False, encoding='utf-8-sig')
\ No newline at end of file
psrc/stage2/good_papers.jsonl deleted 100644 → 0
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psrc/stage2/new_niuren_format-merged_turing.csv deleted 100644 → 0
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psrc/stage2/readme.md deleted 100644 → 0
### 3-find_celebrity_from_paper_with_authors.py
运行：`python 3-find_celebrity_from_paper_with_authors.py`
功能：为`good_papers.jsonl`文件中的每篇论文增加niuren_authors（作者索引列表，如[2,6]表示第2个和第6个是牛人，从1开始编号）和各头衔（如"1.2"表示牛人作者中的第1个和第2个是对应头衔，从1开始编号），得到`good_papers-niuren.jsonl`.
输入：`good_papers.jsonl`，每一行格式为
```python
{"title": "Automating multi-task learning on optical neural networks with weight sharing and physical rotation", "authors": [{"name": "Shanglin Zhou", "affiliations": ["School of Computing, University of Connecticut, Storrs 06269, USA"]}, {"name": "Yingjie Li", "affiliations": ["A. James Clark School of Engineering, University of Maryland, College Park 20742, USA"]}, {"name": "Weilu Gao", "affiliations": ["Electrical and Computer Engineering, University of Utah, Salt Lake City 84112, USA"]}, {"name": "Cunxi Yu", "affiliations": ["A. James Clark School of Engineering, University of Maryland, College Park 20742, USA"]}, {"name": "Caiwen Ding", "affiliations": ["Department of Computer Science & Engineering, University of Minnesota Twin Cities, Minneapolis 55455, USA"]}], "filename": "0001-0001-Automating multi-task learning on optical neural networks with weight sharing and physical rotation.pdf"}
```
输出：`good_papers-niuren.jsonl`，每一行格式为
```python
{"title": "On Efficiency, Fairness and Security in AI Accelerator Resource Sharing: A Survey", "authors": [{"name": "Jiahua Huang", "affiliations": ["South China University of Technology, China", "Pengcheng Laboratory, Guangzhou, China"]}, {"name": "Weiwei Lin", "affiliations": ["South China University of Technology, China", "Pengcheng Laboratory, Guangzhou, China"]}, {"name": "Wentai Wu", "affiliations": ["Department of Computer Science, Jinan University, Guangzhou, China"]}, {"name": "Yang Wang", "affiliations": ["Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, China"]}, {"name": "Haocheng Zhong", "affiliations": ["South China University of Technology, Guangzhou, China"]}, {"name": "Xinhua Wang", "affiliations": ["South China University of Technology, Guangzhou, China"]}, {"name": "Keqin Li", "affiliations": ["Department of Computer Science, State University of New York, New Paltz, United States"]}], "filename": "0001-0009-On Efficiency Fairness and Security in AI Accelerator Resource Sharing A Survey.pdf", "niuren_authors": [4, 7], "ACM Fellow": "", "IEEE Fellow": "1; 2", "AAAS": "", "NAS ": "", "NAE": "", "NAI": "", "American Academy of Arts and Sciences": "", "European Academy of Sciences": "", "European Academy of Sciences and Arts": "", "Academia Europaea (AE)": "", "中国科学院": "", "中国工程院": "", "The Royal Society": "", "The Royal Society of Canada": "", "Turing Award": ""}
```
### 4-papers_with_niuren-final-to_csv.py
运行：`python 4-papers_with_niuren-final-to_csv.py`
功能：将`good_papers-niuren.jsonl`由jsonl格式转换为csv格式`good_papers-niuren.csv`。
输入：good_papers-niuren.jsonl
输出：good_papers-niuren.csv
### 5-find_location_of_papers_with_niuren.py
运行：`python 5-find_location_of_papers_with_niuren.py`
功能：给`good_papers-niuren.csv`文件添加`continent_country`列，得到`good_papers-niuren-task_10.csv`
输入：good_papers-niuren.csv
输出：good_papers-niuren-task_10.csv
注意：第3步因为有一些机构无法和`机构国家汇总-初版.xlsx`中的机构匹配，因此需要手动检查`good_papers-niuren-task_10.csv`的`continent_country`列的空行，手动添加机构（但应该只是很少量的）
### 6-zhimingqiye.py
运行：`python 6-zhimingqiye.py`
功能：给`good_papers-niuren-task_10.csv`文件添加`知名企业`列，得到`good_papers-niuren-task_11.csv`
### 8-add_country_hpca.py
运行：`python 8-add_country_hpca.py`
功能：给`hpca_1-15.csv`文件增加`第一作者洲-国家`列和`通讯作者洲-国家`列
输入：`hpca_1-15.csv`，包括"届数、论文名称、第一作者、第一作者机构、通讯作者、通讯作者结构"这几列
psrc/stage2/utils.py deleted 100644 → 0
def contains_chinese(s_str):
try:
for char in s_str:
if '\u4e00' <= char <= '\u9fff' or \
'\u3400' <= char <= '\u4dbf' or \
'\u20000' <= char <= '\u2a6df':
return True
return False
except Exception as e:
breakpoint()
from pypinyin import pinyin, Style
def chinese_to_english_name(chinese_name):
# 将中文名字分割为姓和名
# 假设姓为第一个字，名为后面的字
if len(chinese_name) < 2:
raise ValueError("名字长度不足")
last_name = chinese_name[0] # 姓
first_name = chinese_name[1:] # 名
# 使用 pypinyin 转换为拼音
last_name_pinyin = pinyin(last_name, style=Style.NORMAL, heteronym=False)[0][0].capitalize()
first_name_pinyin = ''.join([word[0].capitalize() for word in pinyin(first_name, style=Style.NORMAL, heteronym=False)])
# 格式化为 "Last Name, First Name"
english_name = f"{last_name_pinyin}, {first_name_pinyin}"
return english_name
def firstlast2lastfirst(name):
if "," in name or contains_chinese(name):
return name
# 名人列表中存在一些名字是First Name Last Name的格式，将其转换为Last Name, First Name格式
# 例如：'John Doe' -> 'Doe, John'，'M. Jane Smith' -> 'Smith, Jane M.'
special_str = [".", "Ms.", "Mr.", "Mrs.", "Dr.", "Prof.", "PhD", "MD", "Jr.", "Sr.", "The", "Honorable"]
name_split = name.split(" ")
name_split = [item.strip() for item in name_split if item.strip() != ""]
name_split = [item for item in name_split if item not in special_str]
# Grigory Isaakovich Barenblatt --> Barenblatt, Alexa, Marc Isaakovich
if len(name_split) == 2:
first_name = name_split[0]
last_name = name_split[1]
new_name = f"{last_name}, {first_name}"
elif len(name_split) == 3:
if name_split[0].startswith("(") and name_split[0].endswith(")"):
# (Alexander) Philip Dawid
first_name = f"{name_split[1]} {name_split[0]}"
last_name = f"{name_split[2]}"
elif name_split[1].startswith("(") and name_split[1].endswith(")"):
# Xinyan (Tracy) Cui --> Cui, Xinyan (Tracy)
first_name = f"{name_split[0]} {name_split[1]}"
last_name = f"{name_split[2]}"
elif name_split[2].startswith("(") and name_split[2].endswith(")"):
# Ye Fred (Ying)实际是姓Ye，名Ying，英文名Fred, 需要将其转换为"Ye, Ying Fred"
# Zhu Jesse (Jingxu)实际是姓Zhu，名Jingxu，英文名Jesse, 需要将其转换为"Zhu, Jingxu Jesse"
first_name = f"{name_split[2][1:-1]} ({name_split[1]})"
last_name = f"{name_split[0]}"
elif name_split[0].endswith("."):
# M. Jane Smith --> Smith, Jane M.
# K.W. Michael Siu --> Siu, Michael K.W.
first_name = f"{name_split[1]} {name_split[0]}"
last_name = name_split[2]
elif name_split[1].endswith("."):
# Jane M. Smith --> Smith, Jane M.
# Pierre J.H. Richardson --> Richardson, Pierre J.H.
first_name = f"{name_split[0]} {name_split[1]}"
last_name = name_split[2]
elif name_split[2].endswith("."):
# Wimmer-Schweingruber Robert F. --> Wimmer-Schweingruber, Robert F.
# Wilderer Peter A. --> Wilderer, Peter A.
first_name = f"{name_split[1]} {name_split[2]}"
last_name = f"{name_split[0]}"
else:
# William Nelson Joy --> Joy, William Nelson
# Michael J Carey --> Carey, Michael J
# len_3_list.append(" ".join(name_split))
first_name = f"{name_split[0]} {name_split[1]}"
last_name = name_split[2]
new_name = f"{last_name}, {first_name}"
# print(f"{name} --> {new_name}")
else:
# lens greater than 3 这部分名字应该不会跟其他部分名字出现重复
last_name = name_split[-1]
first_name = " ".join(name_split[:-1])
new_name = f"{last_name}, {first_name}"
return new_name
import numpy as np
def rule_of_same_names(item, row_values):
# # 把肯定一样的信息行筛掉，如'Polanyi, John', 'Polanyi, John'
# name_set = set([name for idx, name in item])
# # if len(name_set) == len(item):
# # return ""
# if len(name_set) > 1:
# return ""
# else:
# return 1.0 # 1.0表示同一个人，0.0表示不是同一个人
row_values_sum = np.sum(row_values, axis=0)
if all([item < 2 for item in row_values_sum]):
return 1.0
else:
return 0.0
import json
# 创建一个空列表来存储解析后的 JSON 对象
def read_jsonl(file_path):
"""
读取 JSONL 文件并返回解析后的数据
:param file_path: JSONL 文件路径
:return: 解析后的数据列表
"""
data = []
# 逐行读取文件
with open(file_path, 'r', encoding='utf-8') as file:
for line in file:
# 解析每一行的 JSON 数据
json_line = json.loads(line)
data.append(json_line)
return data
def name_in_niuren_list(name, niuren_name_list):
"""
判断名字是否在牛人列表中
:param name: 名字
:param niuren_name_list: 牛人列表
:return: 如果在牛人列表中，返回 True，否则返回 False
"""
for idx, niuren_name in enumerate(niuren_name_list):
if name == niuren_name:
return idx
for idx, niuren_name in enumerate(niuren_name_list):
if niuren_name.startswith(name):
if niuren_name[niuren_name.find(name) + len(name)] == " ":
return idx
if name.startswith(niuren_name):
if name[name.find(niuren_name) + len(niuren_name)] == " ":
return idx
return -1
import pycountry
import pycountry_convert as pc
def get_continent(country_name):
if country_name == "South Korea":
country_name = "Korea"
try:
country = pycountry.countries.get(name=country_name)
if not country:
# 可能是国家名称不规范，尝试使用通用名
for c in pycountry.countries:
if country_name.lower() in c.name.lower():
country = c
break
if not country:
return "未知洲"
country_alpha2 = country.alpha_2
continent_code = pc.country_alpha2_to_continent_code(country_alpha2)
continent_name = pc.convert_continent_code_to_continent_name(continent_code)
return continent_name
except Exception as e:
return f"出错：{e}"
# # 示例
# print(get_continent("China")) # 输出: Asia
# print(get_continent("Germany")) # 输出: Europe
# print(get_continent("Brazil")) # 输出: South America
def parse_name(name):
candidate_names = []
# 按照逗号分割名字
parts = name.split(',')
if len(parts) < 2:
return None # 不符合格式，跳过
last_name = parts[0].strip() # 姓
first_name_part = parts[1].strip() # 名和可能的中间名
# 按照空格分割名和中间名: "FeiFei-Q" 或 "FeiFei Q"
if "-" in first_name_part:
first_name_1 = first_name_part.split('-')[0].strip()
first_name_2 = first_name_part.split('-')[1].strip()
candidate_names.append(f"{last_name}, {first_name_1}")
candidate_names.append(f"{last_name}, {first_name_1}-{first_name_2}")
candidate_names.append(f"{last_name}, {first_name_1} {first_name_2}")
candidate_names.append(f"{last_name}, {first_name_1} {first_name_2}.")
candidate_names.append(f"{last_name}, {first_name_1} {first_name_2[0]}")
candidate_names.append(f"{last_name}, {first_name_1} {first_name_2[0]}.")
if ", " in first_name_part:
first_name_1 = first_name_part.split(', ')[0].strip()
first_name_2 = first_name_part.split(', ')[1].strip()
candidate_names.append(f"{last_name}, {first_name_1}")
candidate_names.append(f"{last_name}, {first_name_1}-{first_name_2}")
candidate_names.append(f"{last_name}, {first_name_1} {first_name_2}")
candidate_names.append(f"{last_name}, {first_name_1} {first_name_2}.")
candidate_names.append(f"{last_name}, {first_name_1} {first_name_2[0]}")
candidate_names.append(f"{last_name}, {first_name_1} {first_name_2[0]}.")
if " " in first_name_part:
first_name_1 = first_name_part.split(' ')[0].strip()
first_name_2 = first_name_part.split(' ')[1].strip()
candidate_names.append(f"{last_name}, {first_name_1}")
candidate_names.append(f"{last_name}, {first_name_1}-{first_name_2}")
candidate_names.append(f"{last_name}, {first_name_1} {first_name_2}")
candidate_names.append(f"{last_name}, {first_name_1} {first_name_2}.")
candidate_names.append(f"{last_name}, {first_name_1} {first_name_2[0]}")
candidate_names.append(f"{last_name}, {first_name_1} {first_name_2[0]}.")
# else:
# candidate_names.append(f"{last_name}, {first_name_part}")
candidate_names.append(f"{last_name}, {first_name_part}")
return candidate_names
\ No newline at end of file
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