Collection of 115 standalone
TikZ figures for illustrating concepts in physics, chemistry and machine learning.
Check out janosh.github.io to search, sort, open in Overleaf and download figures (PDF/SVG/PNG) from this collection.
Have a TikZ image you'd like to share? Submit a PR with a .tex
and metadata .yml
file in the assets/
directory and add yourself to the citation.cff
file.
Files in /scripts
render and compress the standalone .tex
files in /assets
to various formats:
- low + high-res PNG
- SVG
To run the scripts requires the following dependencies:
pdf-compressor
(pip install pdf-compressor
)gs
(GhostScript) (optional, worse compression but needs no API key so less setup thanpdf-compressor
)pdf2svg
(brew install pdf2svg
)convert
(part of ImageMagick)pngquant
(brew install pngquant
)zopflipng
(brew install zopfli
)
To run pdf-compressor
directly or to use it as part of the render-tikz.py
pipeline, you need a free public API key from https://developer.ilovepdf.com. Pass it to pdf-compressor
with:
pdf-compressor --set-api-key project_public_7c854a9db0...
You can cite the Zenodo record using the following BibTeX entry:
@software{riebesell_tikz_2020,
title = {Collection of standalone TikZ images},
author = {Riebesell, Janosh and Bringuier, Stefan},
date = {2020-08-09},
year = {2020},
doi = {10.5281/zenodo.7486911},
url = {https://github.com/janosh/tikz},
note = {10.5281/zenodo.7486911 - https://github.com/janosh/tikz},
version = {0.1.0},
urldate = {2023-01-01}, % optional, replace with your date of access
}