Recent Releases of ai
ai - final_code
This release is a data processing pipeline for a collection of PDF articles related to a topic (remember to change pdfs paths to try it in your computer). The script kcloud.py draws a keyword cloud based on the abstract information, the script vfigures.py creates a visualization showing the number of figures per article, and finally, the script links.py creates a list of the links found in each paper.
Functionality Description The code performs several tasks related to PDF processing, text extraction, word cloud generation, figure counting, link listing and cleanup of temporary directories.
Dependencies
OS Interaction: The os module is used to interact with the operating system, in this case file operations.
Grobid Client: This script uses the Grobid client to interface with the Grobid server, for extracting structured information from PDF documents.
Word Cloud Generation: The WordCloud class from the wordcloud module is used to generate word clouds from extracted text.
Data Visualization: The matplotlib.pyplot module is used for generating visualizations.
HTML and XML Parsing: The BeautifulSoup class from the bs4 module is used for parsing HTML and XML documents.
- Processing Steps
Processing Articles: Each script iterates over a list of PDF files and processes each article using Grobid, saving the output to specified directories.
Extracting Information: The first script extracts abstracts from the processed articles and generates keyword clouds based on the abstracts. The second script counts the number of figures in each article. The third script extracts links from the articles.
Cleanup: After processing all articles, they collectively remove temporary output directories to maintain cleanliness and organization.
- Execution Flow The scripts share a similar execution flow:
They each iterate over the list of PDF files.
For each PDF file, they process the article using Grobid.
Depending on the script, they extract abstracts, count figures, or extract links from the processed article.
Finally, they perform cleanup tasks by removing temporary directories.
- Output The main outputs of the combined release are:
Keyword clouds generated from abstracts.
Counts of figures found in each article.
Lists of links found in each article.
Overall, the combined release provides a comprehensive solution for processing PDF articles, extracting key information, and performing various analyses, catering to different aspects of data exploration and understanding.
- Python
Published by Pokoyokjk almost 2 years ago
ai - code
This release is a data processing pipeline for a collection of PDF articles related to a topic (remember to change pdfs paths to try it in your computer). The script kcloud.py draws a keyword cloud based on the abstract information, the script vfigures.py creates a visualization showing the number of figures per article, and finally, the script links.py creates a list of the links found in each paper.
1. Functionality Description The code performs several tasks related to PDF processing, text extraction, word cloud generation, figure counting, link listing and cleanup of temporary directories.
2. Dependencies - OS Interaction: The os module is used to interact with the operating system, in this case file operations.
Grobid Client: This script uses the Grobid client to interface with the Grobid server, for extracting structured information from PDF documents.
Word Cloud Generation: The WordCloud class from the wordcloud module is used to generate word clouds from extracted text.
Data Visualization: The matplotlib.pyplot module is used for generating visualizations.
HTML and XML Parsing: The BeautifulSoup class from the bs4 module is used for parsing HTML and XML documents.
3. Processing Steps - Processing Articles: Each script iterates over a list of PDF files and processes each article using Grobid, saving the output to specified directories.
Extracting Information: The first script extracts abstracts from the processed articles and generates keyword clouds based on the abstracts. The second script counts the number of figures in each article. The third script extracts links from the articles.
Cleanup: After processing all articles, they collectively remove temporary output directories to maintain cleanliness and organization.
4. Execution Flow The scripts share a similar execution flow: - They each iterate over the list of PDF files.
For each PDF file, they process the article using Grobid.
Depending on the script, they extract abstracts, count figures, or extract links from the processed article.
Finally, they perform cleanup tasks by removing temporary directories.
5. Output The main outputs of the combined release are:
Keyword clouds generated from abstracts.
Counts of figures found in each article.
Lists of links found in each article.
Overall, the combined release provides a comprehensive solution for processing PDF articles, extracting key information, and performing various analyses, catering to different aspects of data exploration and understanding.
- Python
Published by Pokoyokjk about 2 years ago
ai - scripts
This release is a data processing pipeline for a collection of PDF articles related to a topic (remember to change pdfs paths to try it in your computer). The script kcloud.py draws a keyword cloud based on the abstract information, the script vfigures.py creates a visualization showing the number of figures per article, and finally, the script links.py creates a list of the links found in each paper.
1. Functionality Description The code performs several tasks related to PDF processing, text extraction, word cloud generation, figure counting, link listing and cleanup of temporary directories.
2. Dependencies - OS Interaction: The os module is used to interact with the operating system, in this case file operations.
Grobid Client: This script uses the Grobid client to interface with the Grobid server, for extracting structured information from PDF documents.
Word Cloud Generation: The WordCloud class from the wordcloud module is used to generate word clouds from extracted text.
Data Visualization: The matplotlib.pyplot module is used for generating visualizations.
HTML and XML Parsing: The BeautifulSoup class from the bs4 module is used for parsing HTML and XML documents.
3. Processing Steps - Processing Articles: Each script iterates over a list of PDF files and processes each article using Grobid, saving the output to specified directories.
Extracting Information: The first script extracts abstracts from the processed articles and generates keyword clouds based on the abstracts. The second script counts the number of figures in each article. The third script extracts links from the articles.
Cleanup: After processing all articles, they collectively remove temporary output directories to maintain cleanliness and organization.
4. Execution Flow The scripts share a similar execution flow: - They each iterate over the list of PDF files.
For each PDF file, they process the article using Grobid.
Depending on the script, they extract abstracts, count figures, or extract links from the processed article.
Finally, they perform cleanup tasks by removing temporary directories.
5. Output The main outputs of the combined release are:
Keyword clouds generated from abstracts.
Counts of figures found in each article.
Lists of links found in each article.
Overall, the combined release provides a comprehensive solution for processing PDF articles, extracting key information, and performing various analyses, catering to different aspects of data exploration and understanding.
- Python
Published by Pokoyokjk about 2 years ago