matplotwave 0.1.0

A vaporwave-inspired color palette and theme extension for Matplotlib

matplotwave

A Matplotlib extension for vaporwave-inspired color schemes, forked and improved from the unmaintained vapeplot.

Improvements include

• Reordered palette colors for better contrast
• Dark mode
• Smooth colormaps
• Additional palettes
• Scientific notation support Keywords: vaporwave matplotlib, aesthetic color palette, retro colors matplotlib, neon plots, 80s aesthetics visualization, synthwave colormap, vaporwave style charts, retrofuturism, y2k, lofi

Installation

pip install matplotwave

Quick Start

import seaborn as sns
import matplotwave
import matplotlib.pyplot as plt
sns.set_theme()
matplotwave.set_light_theme() #or dark theme
matplotwave.set_palette("windows95")
plt.plot([1, 2, 3, 4], [1, 3, 2, 4])
plt.plot([1, 2, 3, 4], [2, 1, 3, 2])
plt.plot([1, 2, 3, 4], [1.5, 2, 2.5, 3])
plt.show()

Color palettes and examples

vaporwave

Iconic neon pink/blue mix with a lot of different colors.

y2k

Inspired by the y2k-aesthetic

cool

Vibrant magenta and cyan tones g)

crystal_pepsi and neon_crystal_pepsi

Light pastel colors.

Since crystal pepsi can, dependend on the screen, be hard to read on a white background, I either recommend the neon_crystal_pepsi palette, which is just a bit darker: or, if you really want to stick with the soft pastels, the dark mode: <img src="https://github.com/actopozipc/matplotwave/blob/main/Examples/crystal_pepsi_dark.png"/ style="width: 50%;">

windows95

Inspired by the windows 95 operating system.

mallsoft

Soft shopping mall pastels

Jazzcup

Classic 90s jazz cup design colors

Sunset

Warm neon sunset gradient

Avanti

Bold red and blue retro scheme by mike-u

Seapunk

Underwater teal and purple vibes

Documentation

view all palettes

Visualize them:

matplotwave.available()

or just as a list:

print(matplotwave.available(show=False))

View just specific palettes:

matplotwave.view_palette("vaporwave", "windows95", "cool")

Setting the Color Cycle

matplotwave.set_palette("neon_crystal_pepsi")

Colormaps

Colormaps use linear interpolation between the discrete palette colors to produce 256 smooth shades, which makes it also usable for continuous data visualization.

cmap = matplotwave.cmap("y2k")
plt.imshow(data, cmap=cmap)

Theme Management

Some palettes from the original branch like crystal_pepsi use very light colors that can be hard to read on a white background. For these, I recommend the dark theme:

matplotwave.set_dark_theme()

In order to switch back:

matplotwave.set_light_theme()

Obtaining color palettes

Retrieve the list of colors for a palette:

colors = matplotwave.palette("cool")
print(colors)

or a reversed version:

reversed_colors = matplotwave.reverse("cool")

Other

Althrough this was in the original branch, it was never documented properly. Clean up plots by removing spines:

matplotwave.despine(plt.gca())  # Remove top and right spines
matplotwave.despine(plt.gca(), all=True)  # Remove all spines and ticks

Adjust global font size:

matplotwave.font_size(14)

Contribution and Citation

This project is released as open source software under the MIT License. You are free to use, modify, and redistribute the code in both academic and commercial contexts.

Contributions are very welcome: you can contribute by opening issues, submitting pull requests, proposing new palettes, improving documentation, or adding examples and demonstrations.

If you use this project in a scientific publication or other public-facing work, a citation or acknowledgment would be greatly appreciated, since I strongly believe that aesthetically well-designed plots are key to bringing scientific work to a broader audience. Clear, expressive, and visually engaging figures can significantly improve how research is perceived, understood, and shared beyond a narrow expert community, and referencing this project might be a step into this direction.

Aesthetic fonts in matplotlib

TODO

Issues with the old implementation and why I forked it

As mentioned earlier, this is a fork of the vapeplot repository. It had several key issues that lead to this fork:

First of all, some color palettes used very similar colors. Especially when only plotting two datasets, the lines would often look very similar.
Second, as can be seen in one of the examples, some colors are hardly readable on a white background.
And finally, for me the most important, that the colormaps in vapeplot are just cycling 4 to 5 colors, not a real colormap.