pub-ready-plots 0.2.1

Easy publication-ready matplotlib plots for ML papers and posters.

Agustinus' Very Opiniated Publication-Ready Plotting Library

I love TikZ (btw). I love tikzplotlib. I've been an advocate for the latter (proof). However, tikzplotlib is as good as dead. I need to move on.

[!IMPORTANT] Here's what I use now for all my publication needs. This library is designed to be very opiniated. Beauty is in the eye of the beholder. Also, it is very simple, just a single file and that's it.

[!NOTE] Of course I still use TikZ whenever possible (e.g. Fig. 1 in a paper, diagrams, etc.)

Examples

Left: ICML (letter size), single-column layout. Right: A0 landscape poster, 3-column layout.

       

import pub_ready_plots

...

pub_ready_plots.get_context(
    ...
-   layout="icml",
+   layout="poster-landscape",
    ...
)

...

Installation

Still with me? Still want to use this library? Here's how:

pip install pub-ready-plots

Quick Usage

import pub_ready_plots

with pub_ready_plots.get_context(
    width_frac=1,  # between 0 and 1
    height_frac=0.15,  # between 0 and 1
    layout="icml",  # or "iclr", "neurips", "poster-portrait", "poster-landscape"
    single_col=False,  # only works for the "icml" layout
    nrows=1,  # depending on your subplots, default = 1
    ncols=2,  # depending on your subplots, default = 1
    sharey=True,  # Additional keyword args for `plt.subplots`
    override_rc_params={"lines.linewidth": 4.123}  # Overriding rcParams
) as (fig, axs):
    # Do whatever you want with `fig` and `axs`
    ...

    # Once your done, save it, but do NOT set `tight_layout=True`!
    fig.savefig("filename.pdf")

Then in your LaTeX file, include the plot as follows:

\includegraphics[width=\linewidth]{filename.pdf}

[!IMPORTANT] The argument width=\linewidth is crucial!

That's it! But you should use TikZ more. Anyway, see the full, runnable example in examples/simple_plot.py

[!TIP] I recommend using this library in conjunction with [pypalettes]https://github.com/JosephBARBIERDARNAL/pypalettes to avoid the generic blue-orange Matplotlib colors. Distinguish your plots from others!

Using your own styles

Two options:

1. Use this library and update the resulting rc_params dict with your styles.
   • See examples/advanced_usage.py
2. Fork this repo and modify things as you wish.

Other libraries

Check out tueplots if you want a more complex library. My library is designed to achieve what I want in my papers and posters, with as little code as possible. Because of this, it is very forkable and hackable.