Turns bibtex keywords into an academic summary table
Project description
labels2tables extracts keywords from a bibtex file, and uses them to generate an academic summary table comparing the articles.
Example
Input:
@article{duch_quantifying_2010, keywords = {game:soccer, model:network:centrality, open-access}} @article{yamamoto_common_2011, keywords = {game:soccer, model:network:scale-free, open-access}} @article{yaari_hot_2011, keywords = {game:basketball, model:sequence, open-access}}
Transformation:
import labels2tables labels = labels2tables.bib2labels("examples/sport.in.bib") labels2tables.labels2txt(labels, "examples/sport.out.txt")
Output:
======================================================== game model open-access reference ======================================================== soccer network centrality Y duch_quantifying_2010 scale-free Y yamamoto_common_2011 basketball sequence Y yaari_hot_2011 ========================================================
Advanced
The intermediate labels format encodes table data using standard Python dictionaries, lists and tuples. See examples/*.spec.txt for example tables, and how to describe them as a labels dictionary.
Acknowledgements
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