ScriptedStyles

Algorithmic Art + Automation in Python

ScriptedStyles is a Python-based framework for generating and automating digital artwork using mathematical algorithms, image processing, and data-driven visualization methods.

Originally begun as a personal project, it has evolved into a platform for experimenting with:

• Algorithmic generation of patterns, structures, and visual forms.
• Scientific visualization techniques applied creatively.
• Automation pipelines for bulk processing and uploading images (e.g., Instagram, Etsy, Pinterest).
• Exploration of applied math and physics concepts through code.

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Features

• Mathematical and algorithmic artwork generation (fractals, diffusion, tilings, geometric patterns).
• Reproducible workflows built in Python using NumPy, Matplotlib, and SciPy.
• Modular codebase for testing new generative ideas.
• Automation stack for batch-uploading to social and e-commerce platforms.
• Example image sets included in Sample_Images.

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Usage

Requirements:

• Python 3.x
• NumPy, Matplotlib, SciPy

Quick start:

git clone https://github.com/hschn58/ScriptedStyles.git

python3 ScriptedStyles/Codebase/Designs/Releases/Next_release/Heatmap/flowers_v4.py     

Further customization detailed in flowers_v4.py script header overview.

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Generated Samples

Here are a few representative outputs generated with the scripts in this repo:

(Click thumbnails to view full size. See the Sample_Images/ folder for more.)

Real-World Deployment

This not only functioned as a personal project but used to be an active e-commerce automation pipeline:

• Artwork generation → produced in Python using the scripts in this repo.
• Fulfillment → connected via Printify to create physical products.
• Storefront → integrated with Etsy to manage product listings.
• Marketing → automated posting of generated images to Instagram and Facebook to drive engagement.

Links to deployed channels:

• Instagram: @scriptedstylesart
• Facebook: ScriptedStyles
• Etsy: ScriptedStylesArt Shop

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Notes

Many of these scripts were used to generate professional, large, high-resolution images.

• Created images can exceed 50 MB+.
• Depending on the project and your hardware, rendering can take 20–30 minutes (or longer) on a standard laptop.
• Reducing parameters such as dpi or grid_size (when available) can significantly decrease file size and runtime.

License

This project is licensed under the MIT License.