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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

Powered by bibtexparser

Project details


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