python - 从Python中的大量字符串中读取引号内的子字符串

Question

我有以下字符串：

{"name":"INPROCEEDINGS","__typename":"PublicationConferencePaper"},"hasPermiss
ionToLike":true,"hasPermissionToFollow":true,"publicationCategory":"researchSu
mmary","hasPublicFulltexts":false,"canClaim":false,"publicationType":"inProcee
dings","fulltextRequesterCount":0,"requests":{"__pagination__":
[{"offset":0,"limit":1,"list":[]}]},"activeFiguresCount":0,"activeFigures":
{"__pagination__":[{"offset":0,"limit":100,"list":
[]}]},"abstract":"Heterogeneous Multiprocessor System-on-Chip (MPSoC) are 
progressively becoming predominant in most modern mobile devices. These 
devices are required to perform processing of applications within thermal,
 energy and performance constraints. However, most stock power and thermal
 management mechanisms either neglect some of these constraints or rely on 
frequency scaling to achieve energy-efficiency and temperature reduction on 
the device. Although this inefficient technique can reduce temporal thermal
 gradient, but at the same time hurts the performance of the executing task.
 In this paper, we propose a thermal and energy management mechanism which 
achieves reduction in thermal gradient as well as energy-efficiency through 
resource mapping and thread-partitioning of applications with online 
optimization in heterogeneous MPSoCs. The efficacy of the proposed approach is 
experimentally appraised using different applications from Polybench benchmark 
suite on Odroid-XU4 developmental platform. Results show 28% performance 
improvement, 28.32% energy saving and reduced thermal variance of over 76%
 when compared to the existing approaches. Additionally, the method is able to
 free more than 90% in memory storage on the MPSoC, which would have been 
previously utilized to store several task-to-thread mapping 
configurations.","hasRequestedAbstract":false,"lockedFields"

我正在尝试获取"abstract":"和","hasRequestedAbstract"之间的子字符串。为此，我使用以下代码：

    import requests
    #some more codes here........
    to_visit_url = 'https://www.researchgate.net/publication/328749434_TEEM_Online_Thermal-_and_Energy-Efficiency_Management_on_CPU-GPU_MPSoCs'
    this_page = requests.get(to_visit_url)
    content = str(page.content, encoding="utf-8")
    abstract = re.search('\"abstract\":\"(.*)\",\"hasRequestedAbstract\"', content)
    print('Abstract:\n' + str(abstract))

但在抽象变量中，它的值是 None。可能是什么问题？如上所述，如何获取子字符串？

注意：虽然看起来我可以将其读取为 JSON 对象，但这不是一个选项，因为上面提供的示例文本只是完整 html 内容的一小部分，很难从中提取 JSON 对象。

PS页面的完整内容，即page.content，可以从这里下载：https ://docs.google.com/document/d/1awprvKsLPNoV6NZRmCkktYwMwWJo5aujGyNwGhDf7cA/edit?usp=sharing

或者也可以直接从 URL 下载源代码：https ://www.researchgate.net/publication/328749434_TEEM_Online_Thermal-_and_Energy-Efficiency_Management_on_CPU-GPU_MPSoCs

标签： pythonstringrequestsubstringurlrequest

Answer 1

re.search不返回解析结果列表。它返回SRE_Match对象。如果要获取匹配列表，则需要使用re.findall方法。

1. 测试代码

   import re
   import requests
   
   test_pattern = re.compile('\"abstract\":\"(.*)\",\"hasRequestedAbstract\"')
   test_requests = requests.get("https://www.researchgate.net/publication/328749434_TEEM_Online_Thermal-_and_Energy-Efficiency_Management_on_CPU-GPU_MPSoCs")
   
   print(test_pattern.findall(test_requests.text)[0])
   

2. 结果

   'Heterogeneous Multiprocessor System-on-Chip (MPSoC) are progressively becoming predominant in most modern mobile devices. These devices are required to perform processing of applications within thermal, energy and performance constraints. However, most stock power and thermal management mechanisms either neglect some of these constraints or rely on frequency scaling to achieve energy-efficiency and temperature reduction on the device. Although this inefficient technique can reduce temporal thermal gradient, but at the same time hurts the performance of the executing task. In this paper, we propose a thermal and energy management mechanism which achieves reduction in thermal gradient as well as energy-efficiency through resource mapping and thread-partitioning of applications with online optimization in heterogeneous MPSoCs. The efficacy of the proposed approach is experimentally appraised using different applications from Polybench benchmark suite on Odroid-XU4 developmental platform. Results show 28% performance improvement, 28.32% energy saving and reduced thermal variance of over 76% when compared to the existing approaches. Additionally, the method is able to free more than 90% in memory storage on the MPSoC, which would have been previously utilized to store several task-to-thread mapping configurations.'