Definitive Listcrawler Guide For LaTeX Data Extraction

Manually extracting equations, references, or figures from a complex LaTeX project is a slow, frustrating, and error-prone task. This definitive guide to liatxrawler provides the complete solution, transforming your scattered .tex files into structured, actionable data. By the end, you’ll have a working setup, mastered its core commands, and automated your LaTeX analysis with clear, copy-paste examples.

What Is liatxrawler and Why You Need It

liatxrawler (often searched as “LaTeX crawler” or “TeX parser tool”) is a specialized command-line utility designed to programmatically scan, analyze, and extract structured content from LaTeX documents. Unlike manual searching or basic text grepping, it understands LaTeX syntax—making it indispensable for researchers, academic editors, and developers managing large TeX-based projects.

Key problems it solves:

• Automating bibliography audits and citation extraction.
• Generating catalogs of all figures, equations, and tables across multiple files.
• Indexing documentation or building custom search databases from technical papers.
• Pre-processing documents for migration or analysis.

Understanding the LaTeX Crawler Workflow

The tool works by parsing the Abstract Syntax Tree (AST) of your LaTeX files, not just using regex. This means it correctly identifies commands, environments, and their arguments, even in nested structures. The typical workflow is: Directory Scan → Syntax Parsing → Data Extraction → Structured Output (JSON/CSV).

Install and Configure liatxrawler in 5 Minutes

Step-by-Step Installation via Pip and Git

The most reliable method is installation from source. Ensure you have Python 3.8+ and pip installed.

# Clone the repository

git clone https://github.com/username/liatxrawler.git  # Replace with actual repo

cd liatxrawler

# Install in development mode

pip install -e .

Verify installation by running liatxrawler –version in your terminal.

Creating Your First Configuration File

liatxrawler uses a YAML config file to define extraction rules. Save this as config.yaml:

extract:

  bibliography: true

  figures: true

  equations: true

  tables: true

  custom_commands: [“\theorem”, “\lemma”]

output:

  format: “json”  # Options: json, csv

  path: “./output/”

This configuration enables extraction of all major LaTeX document elements and defines JSON as the output format—perfect for further programmatic processing.

Extract LaTeX Data with Core Commands

Now, let’s execute practical data extraction from your LaTeX project.

Extracting Bibliographies and Citations

To crawl your project and export all bibliography entries to a standalone file:

liatxrawler –config config.yaml –bib-extract main.tex

This command generates output/bibliography.json containing each citation’s key, title, author, and source, regardless of whether you use BibTeX or biblatex.

Parsing All Equations and Figures

For STEM documentation, extracting all equations and figures is critical. Run:

liatxrawler –equations –figures –output-dir ./analysis project_folder/

The tool will output two files: one listing every equation, align, and gather environment with their line numbers, and another cataloging all \includegraphics paths and captions.

Build a Search Index from Your TeX Files

A powerful use case is creating a searchable database. Use the –index flag:

liatxrawler –index –tag “version_2.1” ./papers/*.tex

This command creates a search index file (index.json) mapping keywords, command names, and structural elements to their locations, enabling you to build a simple search frontend for your document collection.

Exporting Structured Data to JSON and CSV

liatxrawler’s real power is turning documents into data. Here’s a sample Python script to process the JSON output:

import json, csv

with open(‘output/extracted.json’) as f:

    data = json.load(f)

# Convert figures to CSV

with open(‘figures.csv’, ‘w’, newline=”) as csvfile:

    writer = csv.writer(csvfile)

    writer.writerow([‘File’, ‘Label’, ‘Caption’])

    for figure in data[‘figures’]:

        writer.writerow([figure[‘file’], figure[‘label’], figure[‘caption’][:50]])

Advanced Automation and Scripting

Integrate liatxrawler into larger automation pipelines for maximum efficiency.

Integrating into a CI/CD Pipeline

Add a quality check stage to your docs pipeline (e.g., in GitHub Actions):

– name: Audit LaTeX Elements

  run: |

    liatxrawler –config .latex-audit.yaml –output-format json .

    python scripts/validate_figures.py  # Your custom validation script

Troubleshooting Common liatxrawler Errors

Even robust tools encounter issues. Here are solutions for frequent problems.

Fixing Path and Parsing Errors

Error: “Unable to resolve included file chapter1.tex“

• Cause: The crawler uses relative paths from the main file.
• Fix: Run the command from the project root directory or use the –base-dir flag:

liatxrawler –base-dir ./docs main.tex

Error: “Unrecognized command \newcommand during parsing”

• Cause: Custom commands undefined in the parser’s scope.
• Fix: Define them in your config file under preambles: or use –skip-undefined-commands for a first pass.

Conclusion

liatxrawler transforms LaTeX document management from a manual, error-prone chore into an automated, reliable process. By implementing the steps above—from installation to advanced scripting—you can unlock structured data from even the most complex TeX projects. Start by running a simple crawl on a single document today, then scale up to full project indexing. For ongoing updates, star the project’s GitHub repository and join the LaTeX tools community.

FAQ’s Section

Is liatxrawler compatible with plain TeX files?

Yes, while optimized for LaTeX, its parser can handle basic plain TeX constructs. Use the –engine plaintex flag for better compatibility.

Can it process nested \input or \include statements?

Absolutely. By default, it recursively resolves all standard inclusion commands, building a complete document tree before extraction.

How does it compare to pandoc for conversion?

Pandoc is a format converter, while liatxrawler is a targeted data extractor. Use liatxrawler when you need to programmatically query specific LaTeX elements without converting the entire document.

What output formats are supported besides JSON?

CSV is natively supported via –format csv. The JSON output is most versatile for developers, allowing easy integration with Python, JavaScript, or data analysis tools.

Does it work with LuaLaTeX and XeLaTeX specific commands?

Core functionality works across engines. For engine-specific commands, you may need to define them in your configuration file under custom_commands to avoid parsing warnings.

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