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ads_paperdata_and_visualization.py
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ads_paperdata_and_visualization.py
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# @Time : 2022/11/10 14:33
# @Author : shaowj
# @File : ads_test_2.py
import ads
import os
import datetime as dt
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
token = '********' # ads上获取的token，每个用户每天搜索次数有限制
def query_counts(keywords, query, year, acknowledgements=False):
if acknowledgements:
query = 'ack:' + query
modifiers = ' '.join([f'year:{year}'])
full_query = ' '.join([f"abs:('{query}')", modifiers])
# print(modifiers)
# print(full_query)
filter_query = ['database:astronomy',
'property:refereed',
'doctype:(article OR eprint OR inbook OR inproceedings)'] # 加限制条件的地方，doctype有点坑，要写全
papers = ads.SearchQuery(q=full_query, fq=filter_query, token=token, rows=2000, sort="citation_count")
papers.execute()
results_count = int(papers.response.numFound) # 先获取文章数量
# print(count)
citation_count_num = 0 # 文章总引用量初始化为0
# print(papers.articles[0].citation_count)
# ads一次请求rows最大返回2000，所以要做区分，不能忽略2000以外的文章引用量
if results_count <= 2000:
for n in papers.articles:
citation_count_num += n.citation_count
else:
rows_1 = 0
while rows_1 < results_count:
papers_per_page = ads.SearchQuery(q=full_query, fq=filter_query, token=token, rows=2000, start=rows_1,
sort="citation_count")
for n in papers_per_page:
citation_count_num += n.citation_count
rows_1 += 2000
print(modifiers, full_query, results_count, citation_count_num)
return dict(keywords=keywords, query=query, year=year, count=results_count, citation_count_num=citation_count_num)
# 示例 'LAMOST': ['LAMOST', 'lamost', 'Lamost'],好在ads不区分大小写（应该是）
DATA = {
'LAMOST': ['LAMOST'],
'SDSS': ['SDSS'],
'SDSS_Official': ['"BOSS" OR "APOGEE" OR "eBOSS" OR "MARVELS" OR "MANGA" OR "SDSS" OR ("Sloan" AND "Survey")) OR '
'title:("BOSS" OR "APOGEE" OR "eBOSS" OR "MARVELS" OR "MANGA" OR "SDSS" OR ("Sloan" AND '
'"Survey")'],
'SDSS Spectrum': ['SDSS Spectrum'],
}
filename = 'ADS_results2.csv'
years = []
[years.append(str(y)) for y in range(1994, 2023)]
years.append('1994-2022')
# print(years)
if not os.path.exists(filename):
results = pd.DataFrame([query_counts(keywords, query, year)
for keywords, queries in DATA.items()
for query in queries
for year in years])
results.to_csv(filename, index=False)
# 绘制
plt.figure(figsize=(10, 6), dpi=100)
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['axes.unicode_minus'] = False
# 读文件
data = pd.read_csv('ADS_results2.csv')
# print(data.loc['SDSS'])
# 读取指定列
xdata = data.loc[:28, 'year'] # 横坐标 时间是列名
# print(xdata)
# y1data = (data.loc[data['keywords'] == 'SDSS', ['count']])[:-1] # 多条曲线的y值 参数名为csv的列名
y3data = (data.loc[data['keywords'] == 'SDSS_Official', ['count']])[:-1] # 多条曲线的y值 参数名为csv的列名
y2data = (data.loc[data['keywords'] == 'SDSS Spectrum', ['count']])[:-1] # 多条曲线的y值 参数名为csv的列名
y4data = (data.loc[data['keywords'] == 'LAMOST', ['count']])[:-1] # 多条曲线的y值 参数名为csv的列名
# color可自定义折线颜色，marker可自定义点形状，label为折线标注
# plt.subplot(2, 1, 1)
# plt.plot(xdata, y1data, color='r', mec='r', mfc='w', label=u'SDSS ALL')
plt.plot(xdata, y3data, color='r', mec='r', mfc='w', label=u'SDSS ALL')
plt.plot(xdata, y2data, color='#7B68EE', mec='r', mfc='w', label=u'SDSS Spectrum')
plt.plot(xdata, y4data, color='c', mec='r', mfc='w', label=u'LAMOST')
plt.title(u"论文数趋势图", size=10)
# 其中参数loc用于设置legend的位置，bbox_to_anchor用于在bbox_transform坐标（默认轴坐标）中为图例指定任意位置。
plt.legend(loc=1, bbox_to_anchor=(0.2, 0.95), borderaxespad=0, frameon=False)
plt.xlabel(u'年份', size=10)
# 设置x轴标签旋转角度和字体大小
plt.xticks(rotation=60, fontsize=8)
plt.ylabel(u'论文数', size=10)
# # y11data = (data.loc[data['keywords'] == 'SDSS', ['citation_count_num']])[:-1] # 多条曲线的y值 参数名为csv的列名
# y33data = (data.loc[data['keywords'] == 'SDSS_Official', ['citation_count_num']])[:-1] # 多条曲线的y值 参数名为csv的列名
# y22data = (data.loc[data['keywords'] == 'SDSS Spectrum', ['citation_count_num']])[:-1] # 多条曲线的y值 参数名为csv的列名
# y44data = (data.loc[data['keywords'] == 'LAMOST', ['citation_count_num']])[:-1] # 多条曲线的y值 参数名为csv的列名
# #
# # color可自定义折线颜色，marker可自定义点形状，label为折线标注
# # plt.subplot(2, 1, 2)
# # plt.plot(xdata, y11data, color='r', mec='r', mfc='w', label=u'SDSS ALL')
# plt.plot(xdata, y33data, color='r', mec='r', mfc='w', label=u'SDSS ALL')
# plt.plot(xdata, y22data, color='#7B68EE', mec='r', mfc='w', label=u'SDSS Spectrum')
# plt.plot(xdata, y44data, color='c', mec='r', mfc='w', label=u'LAMOST')
# plt.title(u"引用数趋势图", size=10)
# # 其中参数loc用于设置legend的位置，bbox_to_anchor用于在bbox_transform坐标（默认轴坐标）中为图例指定任意位置。
# plt.legend(loc=1, bbox_to_anchor=(0.2, 0.95), borderaxespad=0, frameon=False)
# plt.xlabel(u'年份', size=10)
# # 设置x轴标签旋转角度和字体大小
# plt.xticks(rotation=60, fontsize=8)
# plt.ylabel(u'引用数', size=10)
# plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=0.5) # 调整两张子图的距离 不然容易重叠
plt.show()