artpack

Python port of the R package artpack, bringing easier generative art to the Python ecosystem.

About

The goal of artpack is to help generative artists of all levels create generative art in Python. The artpack package is intended for use with the plotnine package.

artpack provides curated color palettes designed for generative art and data visualization. This is a work-in-progress port of the original R package.

Installation

pip install artpack-py

Quick Start

import numpy as np
import plotnine as p9
from polars import concat

from artpack import art_pals, circle_data

# Get colors from palettes
colors_random = art_pals("rainbow", n=10, randomize=True)

# Prepare data
x_positions = range(1, 11)
# Create evenly spaced values between 0.1 and 0.5
base_sizes = np.linspace(0.1, 0.5, 10)
# Randomly sample sizes with replacement
sizes = np.random.choice(base_sizes, size=10, replace=True)

# Create circle data for each position
circles = [
    circle_data(
        x=x_pos,
        y=0,
        fill=color,
        color="#000000",
        radius=size,
        group_var=True,
        group_value=f"Circle_{x_pos:02d}",
    )
    for x_pos, color, size in zip(x_positions, colors_random, sizes)
]

# Combine all circle dataframes into one
df = concat(circles)

# Create plot
(
    p9.ggplot(df, p9.aes("x", "y", group="group"))
    + p9.theme_void()  # Remove axis labels and background
    + p9.theme(
        plot_background=p9.element_rect(
            fill="#222222", color="#111111", size=5
        )
    )
    + p9.geom_polygon(fill=df["fill"], color=df["color"], size=1)
    + p9.coord_equal(expand=False, xlim=(0, 11))  # Equal aspect ratio
)

Development Status

🚧 Work in Progress - Currently porting core functionality from the R version. More features coming soon!

Currently implemented:

• ✅ Color palette generation (art_pals())
• ✅ Circle data generation (circle_data())
• ✅ 100% test coverage
• ✅ CI/CD with GitHub Actions

Roadmap:

• Additional color palette tools (Functions that help with color-related tasks.)
• Asset creation (Functions that help with making data for generative art.)
• Geometric testing tools (Functions that help with geometric/spatial analysis for generative art.)
• Grouping tools (Functions that help with grouping generative art data.)
• Sequencing tools (Functions that help with numeric sequencing.)
• Transformation tools (Functions that help with transforming existing generative art data.)

Relevant Links

• Python Package (PyPI): artpack-py
• Original R Package (CRAN): artpack
• GitHub Repository: github.com/Meghansaha/artpack-py