awesome-latex-drawing
A collection of 30+ LaTeX drawing examples for Bayesian networks, graphical models, tensors, and academic illustrations.
What is awesome-latex-drawing?
Awesome LaTeX Drawing is a collection of over 30 LaTeX code examples for creating professional academic illustrations. It solves the problem of drawing complex diagrams like Bayesian networks, graphical models, and tensor structures in LaTeX by providing reusable, well-documented templates. These examples help researchers and students produce publication-ready graphics without starting from scratch.
Researchers, graduate students, and academics in fields like machine learning, statistics, and data science who need to create technical illustrations for papers, theses, or presentations using LaTeX.
Developers choose this project because it offers a curated set of real-world, peer-reviewed examples that are immediately usable. It saves significant time compared to searching for scattered TikZ/pgfplots solutions and ensures visual consistency with academic publishing standards.
Overview
Drawing Bayesian networks, graphical models, tensors, technical frameworks, and illustrations in LaTeX.
Use Cases
Best For
- Creating Bayesian network diagrams for machine learning papers
- Visualizing tensor factorization models in academic publications
- Plotting probability distributions with mathematical notation in LaTeX
- Drawing graphical models for time series analysis
- Generating illustrations for matrix-based algorithms
- Preparing technical figures for thesis or dissertation documents
Not Ideal For
- Projects requiring interactive or animated graphics for web or presentations
- Teams that prefer GUI-based diagramming tools like Draw.io for rapid prototyping
- Non-academic contexts where simpler tools like PowerPoint or Google Slides are sufficient
- Developers unfamiliar with LaTeX who need quick, no-code solutions
Pros & Cons
Pros
Each of the 30+ examples is derived from published research papers in machine learning and transportation, ensuring relevance and peer-reviewed quality for technical publications.
README breaks down each example into preamble and body codes with specific LaTeX commands, making it easy to adapt templates without guessing, as seen in the Bayesian network sections.
Explicitly mentions using Overleaf for reproduction, simplifying compilation for users by avoiding local LaTeX setup hassles, with direct links to .tex files.
Covers niche areas like Bayesian networks, tensor factorizations, and probability plots using tikz and pgfplots, addressing complex illustration needs in data science fields.
Cons
Requires familiarity with LaTeX packages and environments; users without prior experience may struggle with compilation errors or package installations, as the README assumes basic LaTeX knowledge.
Only generates static PDF images, lacking support for modern interactive or animated graphics that tools like matplotlib or web-based frameworks can offer for dynamic presentations.
Examples are heavily skewed towards machine learning and transportation research, with less coverage for other disciplines like biology or physics, limiting generalizability.
Modifying templates beyond minor adjustments requires deep TikZ expertise, as the code involves intricate node positioning and styling that can be daunting for casual users.
Frequently Asked Questions
Related Projects
PlotNeuralNetLatex code for making neural networks diagrams
quiverA modern commutative diagram editor for the web.
matplotlib2tikz:bar_chart: Save matplotlib figures as TikZ/PGFplots for smooth integration into LaTeX.
matlab2tikzThis program converts MATLAB®/Octave figures to TikZ/pgfplots figures for smooth integration into LaTeX.
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